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C# changes for hybrid action spaces (#4587)

* Add hybrid action capability flag (#4576)

* Change BrainParametersProto to support ActionSpec (#4579)

* Assign new BrainParametersProto fields based on capabilities (#4581)

* ActionBuffer with hybrid actions for RemotePolicy (#4592)

* Barracuda inference for hybrid actions (#4611)

* Refactor BarracudaModel loader checks (#4629)

* Export separate nodes for continuous/discrete actions (#4655)

* Separate continuous/discrete actions in AgentActionProto (#4698)

* Force different nodes for new and deprecated action output (#4705)
/fix-conflict-base-env
GitHub 4 年前
当前提交
94c59e31
共有 63 个文件被更改,包括 4049 次插入655 次删除
  1. 6
      com.unity.ml-agents/Runtime/Academy.cs
  2. 8
      com.unity.ml-agents/Runtime/Actuators/ActionSegment.cs
  3. 3
      com.unity.ml-agents/Runtime/Actuators/ActionSpec.cs
  4. 4
      com.unity.ml-agents/Runtime/Actuators/ActuatorManager.cs
  5. 97
      com.unity.ml-agents/Runtime/Actuators/IActionReceiver.cs
  6. 33
      com.unity.ml-agents/Runtime/Agent.cs
  7. 10
      com.unity.ml-agents/Runtime/Agent.deprecated.cs
  8. 68
      com.unity.ml-agents/Runtime/Communicator/GrpcExtensions.cs
  9. 2
      com.unity.ml-agents/Runtime/Communicator/ICommunicator.cs
  10. 12
      com.unity.ml-agents/Runtime/Communicator/RpcCommunicator.cs
  11. 8
      com.unity.ml-agents/Runtime/Communicator/UnityRLCapabilities.cs
  12. 82
      com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/AgentAction.cs
  13. 348
      com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/BrainParameters.cs
  14. 44
      com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Capabilities.cs
  15. 44
      com.unity.ml-agents/Runtime/Inference/ApplierImpl.cs
  16. 237
      com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs
  17. 4
      com.unity.ml-agents/Runtime/Inference/GeneratorImpl.cs
  18. 12
      com.unity.ml-agents/Runtime/Inference/ModelRunner.cs
  19. 35
      com.unity.ml-agents/Runtime/Inference/TensorApplier.cs
  20. 26
      com.unity.ml-agents/Runtime/Inference/TensorGenerator.cs
  21. 15
      com.unity.ml-agents/Runtime/Inference/TensorNames.cs
  22. 20
      com.unity.ml-agents/Runtime/Policies/BarracudaPolicy.cs
  23. 14
      com.unity.ml-agents/Runtime/Policies/RemotePolicy.cs
  24. 12
      com.unity.ml-agents/Tests/Editor/Actuators/ActuatorManagerTests.cs
  25. 3
      com.unity.ml-agents/Tests/Editor/DemonstrationTests.cs
  26. 74
      com.unity.ml-agents/Tests/Editor/EditModeTestInternalBrainTensorApplier.cs
  27. 7
      com.unity.ml-agents/Tests/Editor/EditModeTestInternalBrainTensorGenerator.cs
  28. 62
      com.unity.ml-agents/Tests/Editor/ModelRunnerTest.cs
  29. 212
      com.unity.ml-agents/Tests/Editor/ParameterLoaderTest.cs
  30. 2
      com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action_deprecated.nn.meta
  31. 2
      com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_recurr_deprecated.nn.meta
  32. 22
      ml-agents-envs/mlagents_envs/communicator_objects/agent_action_pb2.py
  33. 12
      ml-agents-envs/mlagents_envs/communicator_objects/agent_action_pb2.pyi
  34. 82
      ml-agents-envs/mlagents_envs/communicator_objects/brain_parameters_pb2.py
  35. 45
      ml-agents-envs/mlagents_envs/communicator_objects/brain_parameters_pb2.pyi
  36. 13
      ml-agents-envs/mlagents_envs/communicator_objects/capabilities_pb2.py
  37. 6
      ml-agents-envs/mlagents_envs/communicator_objects/capabilities_pb2.pyi
  38. 21
      ml-agents-envs/mlagents_envs/environment.py
  39. 18
      ml-agents-envs/mlagents_envs/mock_communicator.py
  40. 21
      ml-agents-envs/mlagents_envs/rpc_utils.py
  41. 33
      ml-agents-envs/mlagents_envs/tests/test_rpc_utils.py
  42. 14
      ml-agents/mlagents/trainers/demo_loader.py
  43. 19
      ml-agents/mlagents/trainers/tests/simple_test_envs.py
  44. 8
      ml-agents/mlagents/trainers/tests/tensorflow/test_simple_rl.py
  45. 18
      ml-agents/mlagents/trainers/tests/torch/test_simple_rl.py
  46. 28
      ml-agents/mlagents/trainers/torch/action_model.py
  47. 31
      ml-agents/mlagents/trainers/torch/model_serialization.py
  48. 43
      ml-agents/mlagents/trainers/torch/networks.py
  49. 4
      protobuf-definitions/proto/mlagents_envs/communicator_objects/agent_action.proto
  50. 14
      protobuf-definitions/proto/mlagents_envs/communicator_objects/brain_parameters.proto
  51. 3
      protobuf-definitions/proto/mlagents_envs/communicator_objects/capabilities.proto
  52. 360
      com.unity.ml-agents/Runtime/Inference/BarracudaModelExtensions.cs
  53. 11
      com.unity.ml-agents/Runtime/Inference/BarracudaModelExtensions.cs.meta
  54. 1001
      com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action.onnx
  55. 14
      com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action.onnx.meta
  56. 867
      com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_recurr.onnx
  57. 14
      com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_recurr.onnx.meta
  58. 462
      com.unity.ml-agents/Tests/Editor/TestModels/hybrid0vis53vec_3c_2daction.onnx
  59. 14
      com.unity.ml-agents/Tests/Editor/TestModels/hybrid0vis53vec_3c_2daction.onnx.meta
  60. 0
      /com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action_deprecated.nn
  61. 0
      /com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_recurr_deprecated.nn
  62. 0
      /com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action_deprecated.nn.meta
  63. 0
      /com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_recurr_deprecated.nn.meta

6
com.unity.ml-agents/Runtime/Academy.cs


/// <term>1.2.0</term>
/// <description>Support compression mapping for stacked compressed observations.</description>
/// </item>
/// <item>
/// <term>1.3.0</term>
/// <description>Support hybrid action spaces.</description>
/// </item>
const string k_ApiVersion = "1.2.0";
const string k_ApiVersion = "1.3.0";
/// <summary>
/// Unity package version of com.unity.ml-agents.

8
com.unity.ml-agents/Runtime/Actuators/ActionSegment.cs


System.Array.Clear(Array, Offset, Length);
}
/// <summary>
/// Check if the segment is empty.
/// </summary>
public bool IsEmpty()
{
return Array == null || Array.Length == 0;
}
/// <inheritdoc/>
IEnumerator<T> IEnumerable<T>.GetEnumerator()
{

3
com.unity.ml-agents/Runtime/Actuators/ActionSpec.cs


{
if (NumContinuousActions > 0 && NumDiscreteActions > 0)
{
throw new UnityAgentsException("ActionSpecs must be all continuous or all discrete.");
throw new UnityAgentsException("Hybrid action spaces not supported by the trainer. " +
"ActionSpecs must be all continuous or all discrete.");
}
}
}

4
com.unity.ml-agents/Runtime/Actuators/ActuatorManager.cs


Debug.Assert(
!m_Actuators[i].Name.Equals(m_Actuators[i + 1].Name),
"Actuator names must be unique.");
var first = m_Actuators[i].ActionSpec;
var second = m_Actuators[i + 1].ActionSpec;
Debug.Assert(first.NumContinuousActions > 0 == second.NumContinuousActions > 0,
"Actuators on the same Agent must have the same action SpaceType.");
}
}

97
com.unity.ml-agents/Runtime/Actuators/IActionReceiver.cs


}
/// <summary>
/// Construct an <see cref="ActionBuffers"/> instance with <see cref="ActionSpec"/>. All values are initialized to zeros.
/// /// </summary>
/// <param name="actionSpec">The <see cref="ActionSpec"/> to send to an <see cref="IActionReceiver"/>.</param>
public ActionBuffers(ActionSpec actionSpec)
: this(new ActionSegment<float>(new float[actionSpec.NumContinuousActions]),
new ActionSegment<int>(new int[actionSpec.NumDiscreteActions]))
{ }
/// <summary>
/// Create an <see cref="ActionBuffers"/> instance with ActionSpec and all actions stored as a float array.
/// </summary>
/// <param name="actionSpec"><see cref="ActionSpec"/> of the <see cref="ActionBuffers"/></param>
/// <param name="actions">The float array of all actions, including discrete and continuous actions.</param>
/// <returns>An <see cref="ActionBuffers"/> instance initialized with a <see cref="ActionSpec"/> and a float array.
internal static ActionBuffers FromActionSpec(ActionSpec actionSpec, float[] actions)
{
if (actions == null)
{
return ActionBuffers.Empty;
}
Debug.Assert(actions.Length == actionSpec.NumContinuousActions + actionSpec.NumDiscreteActions,
$"The length of '{nameof(actions)}' does not match the total size of ActionSpec.\n" +
$"{nameof(actions)}.Length: {actions.Length}\n" +
$"{nameof(actionSpec)}: {actionSpec.NumContinuousActions + actionSpec.NumDiscreteActions}");
ActionSegment<float> continuousActionSegment = ActionSegment<float>.Empty;
ActionSegment<int> discreteActionSegment = ActionSegment<int>.Empty;
int offset = 0;
if (actionSpec.NumContinuousActions > 0)
{
continuousActionSegment = new ActionSegment<float>(actions, 0, actionSpec.NumContinuousActions);
offset += actionSpec.NumContinuousActions;
}
if (actionSpec.NumDiscreteActions > 0)
{
int[] discreteActions = new int[actionSpec.NumDiscreteActions];
for (var i = 0; i < actionSpec.NumDiscreteActions; i++)
{
discreteActions[i] = (int)actions[i + offset];
}
discreteActionSegment = new ActionSegment<int>(discreteActions);
}
return new ActionBuffers(continuousActionSegment, discreteActionSegment);
}
/// <summary>
/// Clear the <see cref="ContinuousActions"/> and <see cref="DiscreteActions"/> segments to be all zeros.
/// </summary>
public void Clear()

}
/// <summary>
/// Check if the <see cref="ActionBuffers"/> is empty.
/// </summary>
public bool IsEmpty()
{
return ContinuousActions.IsEmpty() && DiscreteActions.IsEmpty();
}
/// <inheritdoc/>
public override bool Equals(object obj)
{

unchecked
{
return (ContinuousActions.GetHashCode() * 397) ^ DiscreteActions.GetHashCode();
}
}
/// <summary>
/// Packs the continuous and discrete actions into one float array. The array passed into this method
/// must have a Length that is greater than or equal to the sum of the Lengths of
/// <see cref="ContinuousActions"/> and <see cref="DiscreteActions"/>.
/// </summary>
/// <param name="destination">A float array to pack actions into whose length is greater than or
/// equal to the addition of the Lengths of this objects <see cref="ContinuousActions"/> and
/// <see cref="DiscreteActions"/> segments.</param>
public void PackActions(in float[] destination)
{
Debug.Assert(destination.Length >= ContinuousActions.Length + DiscreteActions.Length,
$"argument '{nameof(destination)}' is not large enough to pack the actions into.\n" +
$"{nameof(destination)}.Length: {destination.Length}\n" +
$"{nameof(ContinuousActions)}.Length + {nameof(DiscreteActions)}.Length: {ContinuousActions.Length + DiscreteActions.Length}");
var start = 0;
if (ContinuousActions.Length > 0)
{
Array.Copy(ContinuousActions.Array,
ContinuousActions.Offset,
destination,
start,
ContinuousActions.Length);
start = ContinuousActions.Length;
}
if (start >= destination.Length)
{
return;
}
if (DiscreteActions.Length > 0)
{
Array.Copy(DiscreteActions.Array,
DiscreteActions.Offset,
destination,
start,
DiscreteActions.Length);
}
}
}

33
com.unity.ml-agents/Runtime/Agent.cs


/// <summary>
/// Keeps track of the last vector action taken by the Brain.
/// </summary>
public float[] storedVectorActions;
public ActionBuffers storedVectorActions;
/// <summary>
/// For discrete control, specifies the actions that the agent cannot take.

public void ClearActions()
{
Array.Clear(storedVectorActions, 0, storedVectorActions.Length);
storedVectorActions.Clear();
actionBuffers.PackActions(storedVectorActions);
var continuousActions = storedVectorActions.ContinuousActions;
for (var i = 0; i < actionBuffers.ContinuousActions.Length; i++)
{
continuousActions[i] = actionBuffers.ContinuousActions[i];
}
var discreteActions = storedVectorActions.DiscreteActions;
for (var i = 0; i < actionBuffers.DiscreteActions.Length; i++)
{
discreteActions[i] = actionBuffers.DiscreteActions[i];
}
}
}

InitializeSensors();
}
m_Info.storedVectorActions = new float[m_ActuatorManager.TotalNumberOfActions];
m_Info.storedVectorActions = new ActionBuffers(
new float[m_ActuatorManager.NumContinuousActions],
new int[m_ActuatorManager.NumDiscreteActions]
);
// The first time the Academy resets, all Agents in the scene will be
// forced to reset through the <see cref="AgentForceReset"/> event.

m_CumulativeReward = 0f;
m_RequestAction = false;
m_RequestDecision = false;
Array.Clear(m_Info.storedVectorActions, 0, m_Info.storedVectorActions.Length);
m_Info.storedVectorActions.Clear();
}
/// <summary>

}
else
{
m_ActuatorManager.StoredActions.PackActions(m_Info.storedVectorActions);
m_Info.CopyActions(m_ActuatorManager.StoredActions);
}
UpdateSensors();

/// </param>
public virtual void OnActionReceived(ActionBuffers actions)
{
actions.PackActions(m_LegacyActionCache);
if (!actions.ContinuousActions.IsEmpty())
{
m_LegacyActionCache = actions.ContinuousActions.Array;
}
else
{
m_LegacyActionCache = Array.ConvertAll(actions.DiscreteActions.Array, x => (float)x);
}
OnActionReceived(m_LegacyActionCache);
}

10
com.unity.ml-agents/Runtime/Agent.deprecated.cs


// [Obsolete("GetAction has been deprecated, please use GetStoredActionBuffers, Or GetStoredDiscreteActions.")]
public float[] GetAction()
{
return m_Info.storedVectorActions;
var storedAction = m_Info.storedVectorActions;
if (!storedAction.ContinuousActions.IsEmpty())
{
return storedAction.ContinuousActions.Array;
}
else
{
return Array.ConvertAll(storedAction.DiscreteActions.Array, x => (float)x);
}
}
}
}

68
com.unity.ml-agents/Runtime/Communicator/GrpcExtensions.cs


var agentInfoProto = ai.ToAgentInfoProto();
var agentActionProto = new AgentActionProto();
if (ai.storedVectorActions != null)
if (!ai.storedVectorActions.IsEmpty())
agentActionProto.VectorActions.AddRange(ai.storedVectorActions);
if (!ai.storedVectorActions.ContinuousActions.IsEmpty())
{
agentActionProto.ContinuousActions.AddRange(ai.storedVectorActions.ContinuousActions.Array);
}
if (!ai.storedVectorActions.DiscreteActions.IsEmpty())
{
agentActionProto.DiscreteActions.AddRange(ai.storedVectorActions.DiscreteActions.Array);
}
}
return new AgentInfoActionPairProto

{
var brainParametersProto = new BrainParametersProto
{
VectorActionSize = { bp.VectorActionSize },
VectorActionSpaceType = (SpaceTypeProto)bp.VectorActionSpaceType,
VectorActionSizeDeprecated = { bp.VectorActionSize },
VectorActionSpaceTypeDeprecated = (SpaceTypeProto)bp.VectorActionSpaceType,
brainParametersProto.VectorActionDescriptions.AddRange(bp.VectorActionDescriptions);
brainParametersProto.VectorActionDescriptionsDeprecated.AddRange(bp.VectorActionDescriptions);
}
return brainParametersProto;
}

/// <param name="isTraining">Whether or not the Brain is training.</param>
public static BrainParametersProto ToBrainParametersProto(this ActionSpec actionSpec, string name, bool isTraining)
{
actionSpec.CheckNotHybrid();
if (actionSpec.NumContinuousActions > 0)
var actionSpecProto = new ActionSpecProto
brainParametersProto.VectorActionSize.Add(actionSpec.NumContinuousActions);
brainParametersProto.VectorActionSpaceType = SpaceTypeProto.Continuous;
NumContinuousActions = actionSpec.NumContinuousActions,
NumDiscreteActions = actionSpec.NumDiscreteActions,
};
if (actionSpec.BranchSizes != null)
{
actionSpecProto.DiscreteBranchSizes.AddRange(actionSpec.BranchSizes);
else if (actionSpec.NumDiscreteActions > 0)
brainParametersProto.ActionSpec = actionSpecProto;
var supportHybrid = Academy.Instance.TrainerCapabilities == null || Academy.Instance.TrainerCapabilities.HybridActions;
if (!supportHybrid)
brainParametersProto.VectorActionSize.AddRange(actionSpec.BranchSizes);
brainParametersProto.VectorActionSpaceType = SpaceTypeProto.Discrete;
actionSpec.CheckNotHybrid();
if (actionSpec.NumContinuousActions > 0)
{
brainParametersProto.VectorActionSizeDeprecated.Add(actionSpec.NumContinuousActions);
brainParametersProto.VectorActionSpaceTypeDeprecated = SpaceTypeProto.Continuous;
}
else if (actionSpec.NumDiscreteActions > 0)
{
brainParametersProto.VectorActionSizeDeprecated.AddRange(actionSpec.BranchSizes);
brainParametersProto.VectorActionSpaceTypeDeprecated = SpaceTypeProto.Discrete;
}
}
// TODO handle ActionDescriptions?

{
var bp = new BrainParameters
{
VectorActionSize = bpp.VectorActionSize.ToArray(),
VectorActionDescriptions = bpp.VectorActionDescriptions.ToArray(),
VectorActionSpaceType = (SpaceType)bpp.VectorActionSpaceType
VectorActionSize = bpp.VectorActionSizeDeprecated.ToArray(),
VectorActionDescriptions = bpp.VectorActionDescriptionsDeprecated.ToArray(),
VectorActionSpaceType = (SpaceType)bpp.VectorActionSpaceTypeDeprecated
};
return bp;
}

}
#region AgentAction
public static List<float[]> ToAgentActionList(this UnityRLInputProto.Types.ListAgentActionProto proto)
public static List<ActionBuffers> ToAgentActionList(this UnityRLInputProto.Types.ListAgentActionProto proto)
var agentActions = new List<float[]>(proto.Value.Count);
var agentActions = new List<ActionBuffers>(proto.Value.Count);
agentActions.Add(ap.VectorActions.ToArray());
agentActions.Add(ap.ToActionBuffers());
public static ActionBuffers ToActionBuffers(this AgentActionProto proto)
{
return new ActionBuffers(proto.ContinuousActions.ToArray(), proto.DiscreteActions.ToArray());
}
#endregion
#region Observations

BaseRLCapabilities = proto.BaseRLCapabilities,
ConcatenatedPngObservations = proto.ConcatenatedPngObservations,
CompressedChannelMapping = proto.CompressedChannelMapping,
HybridActions = proto.HybridActions,
};
}

BaseRLCapabilities = rlCaps.BaseRLCapabilities,
ConcatenatedPngObservations = rlCaps.ConcatenatedPngObservations,
CompressedChannelMapping = rlCaps.CompressedChannelMapping,
HybridActions = rlCaps.HybridActions,
};
}

2
com.unity.ml-agents/Runtime/Communicator/ICommunicator.cs


/// <param name="key">A key to identify which behavior actions to get.</param>
/// <param name="agentId">A key to identify which Agent actions to get.</param>
/// <returns></returns>
float[] GetActions(string key, int agentId);
ActionBuffers GetActions(string key, int agentId);
}
}

12
com.unity.ml-agents/Runtime/Communicator/RpcCommunicator.cs


UnityRLOutputProto m_CurrentUnityRlOutput =
new UnityRLOutputProto();
Dictionary<string, Dictionary<int, float[]>> m_LastActionsReceived =
new Dictionary<string, Dictionary<int, float[]>>();
Dictionary<string, Dictionary<int, ActionBuffers>> m_LastActionsReceived =
new Dictionary<string, Dictionary<int, ActionBuffers>>();
// Brains that we have sent over the communicator with agents.
HashSet<string> m_SentBrainKeys = new HashSet<string>();

}
if (!m_LastActionsReceived.ContainsKey(behaviorName))
{
m_LastActionsReceived[behaviorName] = new Dictionary<int, float[]>();
m_LastActionsReceived[behaviorName] = new Dictionary<int, ActionBuffers>();
m_LastActionsReceived[behaviorName][info.episodeId] = null;
m_LastActionsReceived[behaviorName][info.episodeId] = ActionBuffers.Empty;
if (info.done)
{
m_LastActionsReceived[behaviorName].Remove(info.episodeId);

}
}
public float[] GetActions(string behaviorName, int agentId)
public ActionBuffers GetActions(string behaviorName, int agentId)
{
if (m_LastActionsReceived.ContainsKey(behaviorName))
{

}
}
return null;
return ActionBuffers.Empty;
}
/// <summary>

8
com.unity.ml-agents/Runtime/Communicator/UnityRLCapabilities.cs


public bool BaseRLCapabilities;
public bool ConcatenatedPngObservations;
public bool CompressedChannelMapping;
public bool HybridActions;
public UnityRLCapabilities(bool baseRlCapabilities = true, bool concatenatedPngObservations = true, bool compressedChannelMapping = true)
public UnityRLCapabilities(
bool baseRlCapabilities = true,
bool concatenatedPngObservations = true,
bool compressedChannelMapping = true,
bool hybridActions = true)
HybridActions = hybridActions;
}
/// <summary>

82
com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/AgentAction.cs


byte[] descriptorData = global::System.Convert.FromBase64String(
string.Concat(
"CjVtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2FnZW50X2Fj",
"dGlvbi5wcm90bxIUY29tbXVuaWNhdG9yX29iamVjdHMiSwoQQWdlbnRBY3Rp",
"b25Qcm90bxIWCg52ZWN0b3JfYWN0aW9ucxgBIAMoAhINCgV2YWx1ZRgEIAEo",
"AkoECAIQA0oECAMQBEoECAUQBkIlqgIiVW5pdHkuTUxBZ2VudHMuQ29tbXVu",
"aWNhdG9yT2JqZWN0c2IGcHJvdG8z"));
"dGlvbi5wcm90bxIUY29tbXVuaWNhdG9yX29iamVjdHMijAEKEEFnZW50QWN0",
"aW9uUHJvdG8SIQoZdmVjdG9yX2FjdGlvbnNfZGVwcmVjYXRlZBgBIAMoAhIN",
"CgV2YWx1ZRgEIAEoAhIaChJjb250aW51b3VzX2FjdGlvbnMYBiADKAISGAoQ",
"ZGlzY3JldGVfYWN0aW9ucxgHIAMoBUoECAIQA0oECAMQBEoECAUQBkIlqgIi",
"VW5pdHkuTUxBZ2VudHMuQ29tbXVuaWNhdG9yT2JqZWN0c2IGcHJvdG8z"));
new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.AgentActionProto), global::Unity.MLAgents.CommunicatorObjects.AgentActionProto.Parser, new[]{ "VectorActions", "Value" }, null, null, null)
new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.AgentActionProto), global::Unity.MLAgents.CommunicatorObjects.AgentActionProto.Parser, new[]{ "VectorActionsDeprecated", "Value", "ContinuousActions", "DiscreteActions" }, null, null, null)
}));
}
#endregion

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public AgentActionProto(AgentActionProto other) : this() {
vectorActions_ = other.vectorActions_.Clone();
vectorActionsDeprecated_ = other.vectorActionsDeprecated_.Clone();
continuousActions_ = other.continuousActions_.Clone();
discreteActions_ = other.discreteActions_.Clone();
_unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
}

}
/// <summary>Field number for the "vector_actions" field.</summary>
public const int VectorActionsFieldNumber = 1;
private static readonly pb::FieldCodec<float> _repeated_vectorActions_codec
/// <summary>Field number for the "vector_actions_deprecated" field.</summary>
public const int VectorActionsDeprecatedFieldNumber = 1;
private static readonly pb::FieldCodec<float> _repeated_vectorActionsDeprecated_codec
private readonly pbc::RepeatedField<float> vectorActions_ = new pbc::RepeatedField<float>();
private readonly pbc::RepeatedField<float> vectorActionsDeprecated_ = new pbc::RepeatedField<float>();
/// <summary>
/// mark as deprecated in communicator v1.3.0
/// </summary>
public pbc::RepeatedField<float> VectorActions {
get { return vectorActions_; }
public pbc::RepeatedField<float> VectorActionsDeprecated {
get { return vectorActionsDeprecated_; }
}
/// <summary>Field number for the "value" field.</summary>

}
}
/// <summary>Field number for the "continuous_actions" field.</summary>
public const int ContinuousActionsFieldNumber = 6;
private static readonly pb::FieldCodec<float> _repeated_continuousActions_codec
= pb::FieldCodec.ForFloat(50);
private readonly pbc::RepeatedField<float> continuousActions_ = new pbc::RepeatedField<float>();
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public pbc::RepeatedField<float> ContinuousActions {
get { return continuousActions_; }
}
/// <summary>Field number for the "discrete_actions" field.</summary>
public const int DiscreteActionsFieldNumber = 7;
private static readonly pb::FieldCodec<int> _repeated_discreteActions_codec
= pb::FieldCodec.ForInt32(58);
private readonly pbc::RepeatedField<int> discreteActions_ = new pbc::RepeatedField<int>();
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public pbc::RepeatedField<int> DiscreteActions {
get { return discreteActions_; }
}
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public override bool Equals(object other) {
return Equals(other as AgentActionProto);

if (ReferenceEquals(other, this)) {
return true;
}
if(!vectorActions_.Equals(other.vectorActions_)) return false;
if(!vectorActionsDeprecated_.Equals(other.vectorActionsDeprecated_)) return false;
if(!continuousActions_.Equals(other.continuousActions_)) return false;
if(!discreteActions_.Equals(other.discreteActions_)) return false;
return Equals(_unknownFields, other._unknownFields);
}

hash ^= vectorActions_.GetHashCode();
hash ^= vectorActionsDeprecated_.GetHashCode();
hash ^= continuousActions_.GetHashCode();
hash ^= discreteActions_.GetHashCode();
if (_unknownFields != null) {
hash ^= _unknownFields.GetHashCode();
}

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public void WriteTo(pb::CodedOutputStream output) {
vectorActions_.WriteTo(output, _repeated_vectorActions_codec);
vectorActionsDeprecated_.WriteTo(output, _repeated_vectorActionsDeprecated_codec);
continuousActions_.WriteTo(output, _repeated_continuousActions_codec);
discreteActions_.WriteTo(output, _repeated_discreteActions_codec);
if (_unknownFields != null) {
_unknownFields.WriteTo(output);
}

public int CalculateSize() {
int size = 0;
size += vectorActions_.CalculateSize(_repeated_vectorActions_codec);
size += vectorActionsDeprecated_.CalculateSize(_repeated_vectorActionsDeprecated_codec);
size += continuousActions_.CalculateSize(_repeated_continuousActions_codec);
size += discreteActions_.CalculateSize(_repeated_discreteActions_codec);
if (_unknownFields != null) {
size += _unknownFields.CalculateSize();
}

if (other == null) {
return;
}
vectorActions_.Add(other.vectorActions_);
vectorActionsDeprecated_.Add(other.vectorActionsDeprecated_);
continuousActions_.Add(other.continuousActions_);
discreteActions_.Add(other.discreteActions_);
_unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
}

break;
case 10:
case 13: {
vectorActions_.AddEntriesFrom(input, _repeated_vectorActions_codec);
vectorActionsDeprecated_.AddEntriesFrom(input, _repeated_vectorActionsDeprecated_codec);
break;
}
case 50:
case 53: {
continuousActions_.AddEntriesFrom(input, _repeated_continuousActions_codec);
break;
}
case 58:
case 56: {
discreteActions_.AddEntriesFrom(input, _repeated_discreteActions_codec);
break;
}
}

348
com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/BrainParameters.cs


"CjltbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2JyYWluX3Bh",
"cmFtZXRlcnMucHJvdG8SFGNvbW11bmljYXRvcl9vYmplY3RzGjNtbGFnZW50",
"c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL3NwYWNlX3R5cGUucHJvdG8i",
"2QEKFEJyYWluUGFyYW1ldGVyc1Byb3RvEhoKEnZlY3Rvcl9hY3Rpb25fc2l6",
"ZRgDIAMoBRIiChp2ZWN0b3JfYWN0aW9uX2Rlc2NyaXB0aW9ucxgFIAMoCRJG",
"Chh2ZWN0b3JfYWN0aW9uX3NwYWNlX3R5cGUYBiABKA4yJC5jb21tdW5pY2F0",
"b3Jfb2JqZWN0cy5TcGFjZVR5cGVQcm90bxISCgpicmFpbl9uYW1lGAcgASgJ",
"EhMKC2lzX3RyYWluaW5nGAggASgISgQIARACSgQIAhADSgQIBBAFQiWqAiJV",
"bml0eS5NTEFnZW50cy5Db21tdW5pY2F0b3JPYmplY3RzYgZwcm90bzM="));
"iwEKD0FjdGlvblNwZWNQcm90bxIeChZudW1fY29udGludW91c19hY3Rpb25z",
"GAEgASgFEhwKFG51bV9kaXNjcmV0ZV9hY3Rpb25zGAIgASgFEh0KFWRpc2Ny",
"ZXRlX2JyYW5jaF9zaXplcxgDIAMoBRIbChNhY3Rpb25fZGVzY3JpcHRpb25z",
"GAQgAygJIrYCChRCcmFpblBhcmFtZXRlcnNQcm90bxIlCh12ZWN0b3JfYWN0",
"aW9uX3NpemVfZGVwcmVjYXRlZBgDIAMoBRItCiV2ZWN0b3JfYWN0aW9uX2Rl",
"c2NyaXB0aW9uc19kZXByZWNhdGVkGAUgAygJElEKI3ZlY3Rvcl9hY3Rpb25f",
"c3BhY2VfdHlwZV9kZXByZWNhdGVkGAYgASgOMiQuY29tbXVuaWNhdG9yX29i",
"amVjdHMuU3BhY2VUeXBlUHJvdG8SEgoKYnJhaW5fbmFtZRgHIAEoCRITCgtp",
"c190cmFpbmluZxgIIAEoCBI6CgthY3Rpb25fc3BlYxgJIAEoCzIlLmNvbW11",
"bmljYXRvcl9vYmplY3RzLkFjdGlvblNwZWNQcm90b0oECAEQAkoECAIQA0oE",
"CAQQBUIlqgIiVW5pdHkuTUxBZ2VudHMuQ29tbXVuaWNhdG9yT2JqZWN0c2IG",
"cHJvdG8z"));
new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.BrainParametersProto), global::Unity.MLAgents.CommunicatorObjects.BrainParametersProto.Parser, new[]{ "VectorActionSize", "VectorActionDescriptions", "VectorActionSpaceType", "BrainName", "IsTraining" }, null, null, null)
new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.ActionSpecProto), global::Unity.MLAgents.CommunicatorObjects.ActionSpecProto.Parser, new[]{ "NumContinuousActions", "NumDiscreteActions", "DiscreteBranchSizes", "ActionDescriptions" }, null, null, null),
new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.BrainParametersProto), global::Unity.MLAgents.CommunicatorObjects.BrainParametersProto.Parser, new[]{ "VectorActionSizeDeprecated", "VectorActionDescriptionsDeprecated", "VectorActionSpaceTypeDeprecated", "BrainName", "IsTraining", "ActionSpec" }, null, null, null)
}));
}
#endregion

internal sealed partial class ActionSpecProto : pb::IMessage<ActionSpecProto> {
private static readonly pb::MessageParser<ActionSpecProto> _parser = new pb::MessageParser<ActionSpecProto>(() => new ActionSpecProto());
private pb::UnknownFieldSet _unknownFields;
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public static pb::MessageParser<ActionSpecProto> Parser { get { return _parser; } }
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public static pbr::MessageDescriptor Descriptor {
get { return global::Unity.MLAgents.CommunicatorObjects.BrainParametersReflection.Descriptor.MessageTypes[0]; }
}
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
pbr::MessageDescriptor pb::IMessage.Descriptor {
get { return Descriptor; }
}
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public ActionSpecProto() {
OnConstruction();
}
partial void OnConstruction();
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public ActionSpecProto(ActionSpecProto other) : this() {
numContinuousActions_ = other.numContinuousActions_;
numDiscreteActions_ = other.numDiscreteActions_;
discreteBranchSizes_ = other.discreteBranchSizes_.Clone();
actionDescriptions_ = other.actionDescriptions_.Clone();
_unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
}
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public ActionSpecProto Clone() {
return new ActionSpecProto(this);
}
/// <summary>Field number for the "num_continuous_actions" field.</summary>
public const int NumContinuousActionsFieldNumber = 1;
private int numContinuousActions_;
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public int NumContinuousActions {
get { return numContinuousActions_; }
set {
numContinuousActions_ = value;
}
}
/// <summary>Field number for the "num_discrete_actions" field.</summary>
public const int NumDiscreteActionsFieldNumber = 2;
private int numDiscreteActions_;
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public int NumDiscreteActions {
get { return numDiscreteActions_; }
set {
numDiscreteActions_ = value;
}
}
/// <summary>Field number for the "discrete_branch_sizes" field.</summary>
public const int DiscreteBranchSizesFieldNumber = 3;
private static readonly pb::FieldCodec<int> _repeated_discreteBranchSizes_codec
= pb::FieldCodec.ForInt32(26);
private readonly pbc::RepeatedField<int> discreteBranchSizes_ = new pbc::RepeatedField<int>();
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public pbc::RepeatedField<int> DiscreteBranchSizes {
get { return discreteBranchSizes_; }
}
/// <summary>Field number for the "action_descriptions" field.</summary>
public const int ActionDescriptionsFieldNumber = 4;
private static readonly pb::FieldCodec<string> _repeated_actionDescriptions_codec
= pb::FieldCodec.ForString(34);
private readonly pbc::RepeatedField<string> actionDescriptions_ = new pbc::RepeatedField<string>();
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public pbc::RepeatedField<string> ActionDescriptions {
get { return actionDescriptions_; }
}
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public override bool Equals(object other) {
return Equals(other as ActionSpecProto);
}
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public bool Equals(ActionSpecProto other) {
if (ReferenceEquals(other, null)) {
return false;
}
if (ReferenceEquals(other, this)) {
return true;
}
if (NumContinuousActions != other.NumContinuousActions) return false;
if (NumDiscreteActions != other.NumDiscreteActions) return false;
if(!discreteBranchSizes_.Equals(other.discreteBranchSizes_)) return false;
if(!actionDescriptions_.Equals(other.actionDescriptions_)) return false;
return Equals(_unknownFields, other._unknownFields);
}
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public override int GetHashCode() {
int hash = 1;
if (NumContinuousActions != 0) hash ^= NumContinuousActions.GetHashCode();
if (NumDiscreteActions != 0) hash ^= NumDiscreteActions.GetHashCode();
hash ^= discreteBranchSizes_.GetHashCode();
hash ^= actionDescriptions_.GetHashCode();
if (_unknownFields != null) {
hash ^= _unknownFields.GetHashCode();
}
return hash;
}
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public override string ToString() {
return pb::JsonFormatter.ToDiagnosticString(this);
}
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public void WriteTo(pb::CodedOutputStream output) {
if (NumContinuousActions != 0) {
output.WriteRawTag(8);
output.WriteInt32(NumContinuousActions);
}
if (NumDiscreteActions != 0) {
output.WriteRawTag(16);
output.WriteInt32(NumDiscreteActions);
}
discreteBranchSizes_.WriteTo(output, _repeated_discreteBranchSizes_codec);
actionDescriptions_.WriteTo(output, _repeated_actionDescriptions_codec);
if (_unknownFields != null) {
_unknownFields.WriteTo(output);
}
}
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public int CalculateSize() {
int size = 0;
if (NumContinuousActions != 0) {
size += 1 + pb::CodedOutputStream.ComputeInt32Size(NumContinuousActions);
}
if (NumDiscreteActions != 0) {
size += 1 + pb::CodedOutputStream.ComputeInt32Size(NumDiscreteActions);
}
size += discreteBranchSizes_.CalculateSize(_repeated_discreteBranchSizes_codec);
size += actionDescriptions_.CalculateSize(_repeated_actionDescriptions_codec);
if (_unknownFields != null) {
size += _unknownFields.CalculateSize();
}
return size;
}
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public void MergeFrom(ActionSpecProto other) {
if (other == null) {
return;
}
if (other.NumContinuousActions != 0) {
NumContinuousActions = other.NumContinuousActions;
}
if (other.NumDiscreteActions != 0) {
NumDiscreteActions = other.NumDiscreteActions;
}
discreteBranchSizes_.Add(other.discreteBranchSizes_);
actionDescriptions_.Add(other.actionDescriptions_);
_unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
}
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public void MergeFrom(pb::CodedInputStream input) {
uint tag;
while ((tag = input.ReadTag()) != 0) {
switch(tag) {
default:
_unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);
break;
case 8: {
NumContinuousActions = input.ReadInt32();
break;
}
case 16: {
NumDiscreteActions = input.ReadInt32();
break;
}
case 26:
case 24: {
discreteBranchSizes_.AddEntriesFrom(input, _repeated_discreteBranchSizes_codec);
break;
}
case 34: {
actionDescriptions_.AddEntriesFrom(input, _repeated_actionDescriptions_codec);
break;
}
}
}
}
}
internal sealed partial class BrainParametersProto : pb::IMessage<BrainParametersProto> {
private static readonly pb::MessageParser<BrainParametersProto> _parser = new pb::MessageParser<BrainParametersProto>(() => new BrainParametersProto());
private pb::UnknownFieldSet _unknownFields;

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public static pbr::MessageDescriptor Descriptor {
get { return global::Unity.MLAgents.CommunicatorObjects.BrainParametersReflection.Descriptor.MessageTypes[0]; }
get { return global::Unity.MLAgents.CommunicatorObjects.BrainParametersReflection.Descriptor.MessageTypes[1]; }
}
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public BrainParametersProto(BrainParametersProto other) : this() {
vectorActionSize_ = other.vectorActionSize_.Clone();
vectorActionDescriptions_ = other.vectorActionDescriptions_.Clone();
vectorActionSpaceType_ = other.vectorActionSpaceType_;
vectorActionSizeDeprecated_ = other.vectorActionSizeDeprecated_.Clone();
vectorActionDescriptionsDeprecated_ = other.vectorActionDescriptionsDeprecated_.Clone();
vectorActionSpaceTypeDeprecated_ = other.vectorActionSpaceTypeDeprecated_;
ActionSpec = other.actionSpec_ != null ? other.ActionSpec.Clone() : null;
_unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
}

}
/// <summary>Field number for the "vector_action_size" field.</summary>
public const int VectorActionSizeFieldNumber = 3;
private static readonly pb::FieldCodec<int> _repeated_vectorActionSize_codec
/// <summary>Field number for the "vector_action_size_deprecated" field.</summary>
public const int VectorActionSizeDeprecatedFieldNumber = 3;
private static readonly pb::FieldCodec<int> _repeated_vectorActionSizeDeprecated_codec
private readonly pbc::RepeatedField<int> vectorActionSize_ = new pbc::RepeatedField<int>();
private readonly pbc::RepeatedField<int> vectorActionSizeDeprecated_ = new pbc::RepeatedField<int>();
/// <summary>
/// mark as deprecated in communicator v1.3.0
/// </summary>
public pbc::RepeatedField<int> VectorActionSize {
get { return vectorActionSize_; }
public pbc::RepeatedField<int> VectorActionSizeDeprecated {
get { return vectorActionSizeDeprecated_; }
/// <summary>Field number for the "vector_action_descriptions" field.</summary>
public const int VectorActionDescriptionsFieldNumber = 5;
private static readonly pb::FieldCodec<string> _repeated_vectorActionDescriptions_codec
/// <summary>Field number for the "vector_action_descriptions_deprecated" field.</summary>
public const int VectorActionDescriptionsDeprecatedFieldNumber = 5;
private static readonly pb::FieldCodec<string> _repeated_vectorActionDescriptionsDeprecated_codec
private readonly pbc::RepeatedField<string> vectorActionDescriptions_ = new pbc::RepeatedField<string>();
private readonly pbc::RepeatedField<string> vectorActionDescriptionsDeprecated_ = new pbc::RepeatedField<string>();
/// <summary>
/// mark as deprecated in communicator v1.3.0
/// </summary>
public pbc::RepeatedField<string> VectorActionDescriptions {
get { return vectorActionDescriptions_; }
public pbc::RepeatedField<string> VectorActionDescriptionsDeprecated {
get { return vectorActionDescriptionsDeprecated_; }
/// <summary>Field number for the "vector_action_space_type" field.</summary>
public const int VectorActionSpaceTypeFieldNumber = 6;
private global::Unity.MLAgents.CommunicatorObjects.SpaceTypeProto vectorActionSpaceType_ = 0;
/// <summary>Field number for the "vector_action_space_type_deprecated" field.</summary>
public const int VectorActionSpaceTypeDeprecatedFieldNumber = 6;
private global::Unity.MLAgents.CommunicatorObjects.SpaceTypeProto vectorActionSpaceTypeDeprecated_ = 0;
/// <summary>
/// mark as deprecated in communicator v1.3.0
/// </summary>
public global::Unity.MLAgents.CommunicatorObjects.SpaceTypeProto VectorActionSpaceType {
get { return vectorActionSpaceType_; }
public global::Unity.MLAgents.CommunicatorObjects.SpaceTypeProto VectorActionSpaceTypeDeprecated {
get { return vectorActionSpaceTypeDeprecated_; }
vectorActionSpaceType_ = value;
vectorActionSpaceTypeDeprecated_ = value;
}
}

}
}
/// <summary>Field number for the "action_spec" field.</summary>
public const int ActionSpecFieldNumber = 9;
private global::Unity.MLAgents.CommunicatorObjects.ActionSpecProto actionSpec_;
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public global::Unity.MLAgents.CommunicatorObjects.ActionSpecProto ActionSpec {
get { return actionSpec_; }
set {
actionSpec_ = value;
}
}
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public override bool Equals(object other) {
return Equals(other as BrainParametersProto);

if (ReferenceEquals(other, this)) {
return true;
}
if(!vectorActionSize_.Equals(other.vectorActionSize_)) return false;
if(!vectorActionDescriptions_.Equals(other.vectorActionDescriptions_)) return false;
if (VectorActionSpaceType != other.VectorActionSpaceType) return false;
if(!vectorActionSizeDeprecated_.Equals(other.vectorActionSizeDeprecated_)) return false;
if(!vectorActionDescriptionsDeprecated_.Equals(other.vectorActionDescriptionsDeprecated_)) return false;
if (VectorActionSpaceTypeDeprecated != other.VectorActionSpaceTypeDeprecated) return false;
if (!object.Equals(ActionSpec, other.ActionSpec)) return false;
return Equals(_unknownFields, other._unknownFields);
}

hash ^= vectorActionSize_.GetHashCode();
hash ^= vectorActionDescriptions_.GetHashCode();
if (VectorActionSpaceType != 0) hash ^= VectorActionSpaceType.GetHashCode();
hash ^= vectorActionSizeDeprecated_.GetHashCode();
hash ^= vectorActionDescriptionsDeprecated_.GetHashCode();
if (VectorActionSpaceTypeDeprecated != 0) hash ^= VectorActionSpaceTypeDeprecated.GetHashCode();
if (actionSpec_ != null) hash ^= ActionSpec.GetHashCode();
if (_unknownFields != null) {
hash ^= _unknownFields.GetHashCode();
}

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public void WriteTo(pb::CodedOutputStream output) {
vectorActionSize_.WriteTo(output, _repeated_vectorActionSize_codec);
vectorActionDescriptions_.WriteTo(output, _repeated_vectorActionDescriptions_codec);
if (VectorActionSpaceType != 0) {
vectorActionSizeDeprecated_.WriteTo(output, _repeated_vectorActionSizeDeprecated_codec);
vectorActionDescriptionsDeprecated_.WriteTo(output, _repeated_vectorActionDescriptionsDeprecated_codec);
if (VectorActionSpaceTypeDeprecated != 0) {
output.WriteEnum((int) VectorActionSpaceType);
output.WriteEnum((int) VectorActionSpaceTypeDeprecated);
}
if (BrainName.Length != 0) {
output.WriteRawTag(58);

output.WriteRawTag(64);
output.WriteBool(IsTraining);
}
if (actionSpec_ != null) {
output.WriteRawTag(74);
output.WriteMessage(ActionSpec);
}
if (_unknownFields != null) {
_unknownFields.WriteTo(output);
}

public int CalculateSize() {
int size = 0;
size += vectorActionSize_.CalculateSize(_repeated_vectorActionSize_codec);
size += vectorActionDescriptions_.CalculateSize(_repeated_vectorActionDescriptions_codec);
if (VectorActionSpaceType != 0) {
size += 1 + pb::CodedOutputStream.ComputeEnumSize((int) VectorActionSpaceType);
size += vectorActionSizeDeprecated_.CalculateSize(_repeated_vectorActionSizeDeprecated_codec);
size += vectorActionDescriptionsDeprecated_.CalculateSize(_repeated_vectorActionDescriptionsDeprecated_codec);
if (VectorActionSpaceTypeDeprecated != 0) {
size += 1 + pb::CodedOutputStream.ComputeEnumSize((int) VectorActionSpaceTypeDeprecated);
}
if (BrainName.Length != 0) {
size += 1 + pb::CodedOutputStream.ComputeStringSize(BrainName);

}
if (actionSpec_ != null) {
size += 1 + pb::CodedOutputStream.ComputeMessageSize(ActionSpec);
}
if (_unknownFields != null) {
size += _unknownFields.CalculateSize();

if (other == null) {
return;
}
vectorActionSize_.Add(other.vectorActionSize_);
vectorActionDescriptions_.Add(other.vectorActionDescriptions_);
if (other.VectorActionSpaceType != 0) {
VectorActionSpaceType = other.VectorActionSpaceType;
vectorActionSizeDeprecated_.Add(other.vectorActionSizeDeprecated_);
vectorActionDescriptionsDeprecated_.Add(other.vectorActionDescriptionsDeprecated_);
if (other.VectorActionSpaceTypeDeprecated != 0) {
VectorActionSpaceTypeDeprecated = other.VectorActionSpaceTypeDeprecated;
}
if (other.BrainName.Length != 0) {
BrainName = other.BrainName;

}
if (other.actionSpec_ != null) {
if (actionSpec_ == null) {
actionSpec_ = new global::Unity.MLAgents.CommunicatorObjects.ActionSpecProto();
}
ActionSpec.MergeFrom(other.ActionSpec);
}
_unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
}

break;
case 26:
case 24: {
vectorActionSize_.AddEntriesFrom(input, _repeated_vectorActionSize_codec);
vectorActionSizeDeprecated_.AddEntriesFrom(input, _repeated_vectorActionSizeDeprecated_codec);
vectorActionDescriptions_.AddEntriesFrom(input, _repeated_vectorActionDescriptions_codec);
vectorActionDescriptionsDeprecated_.AddEntriesFrom(input, _repeated_vectorActionDescriptionsDeprecated_codec);
vectorActionSpaceType_ = (global::Unity.MLAgents.CommunicatorObjects.SpaceTypeProto) input.ReadEnum();
vectorActionSpaceTypeDeprecated_ = (global::Unity.MLAgents.CommunicatorObjects.SpaceTypeProto) input.ReadEnum();
break;
}
case 58: {

case 64: {
IsTraining = input.ReadBool();
break;
}
case 74: {
if (actionSpec_ == null) {
actionSpec_ = new global::Unity.MLAgents.CommunicatorObjects.ActionSpecProto();
}
input.ReadMessage(actionSpec_);
break;
}
}

44
com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/Capabilities.cs


byte[] descriptorData = global::System.Convert.FromBase64String(
string.Concat(
"CjVtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2NhcGFiaWxp",
"dGllcy5wcm90bxIUY29tbXVuaWNhdG9yX29iamVjdHMifQoYVW5pdHlSTENh",
"cGFiaWxpdGllc1Byb3RvEhoKEmJhc2VSTENhcGFiaWxpdGllcxgBIAEoCBIj",
"Chtjb25jYXRlbmF0ZWRQbmdPYnNlcnZhdGlvbnMYAiABKAgSIAoYY29tcHJl",
"c3NlZENoYW5uZWxNYXBwaW5nGAMgASgIQiWqAiJVbml0eS5NTEFnZW50cy5D",
"b21tdW5pY2F0b3JPYmplY3RzYgZwcm90bzM="));
"dGllcy5wcm90bxIUY29tbXVuaWNhdG9yX29iamVjdHMilAEKGFVuaXR5UkxD",
"YXBhYmlsaXRpZXNQcm90bxIaChJiYXNlUkxDYXBhYmlsaXRpZXMYASABKAgS",
"IwobY29uY2F0ZW5hdGVkUG5nT2JzZXJ2YXRpb25zGAIgASgIEiAKGGNvbXBy",
"ZXNzZWRDaGFubmVsTWFwcGluZxgDIAEoCBIVCg1oeWJyaWRBY3Rpb25zGAQg",
"ASgIQiWqAiJVbml0eS5NTEFnZW50cy5Db21tdW5pY2F0b3JPYmplY3RzYgZw",
"cm90bzM="));
new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.UnityRLCapabilitiesProto), global::Unity.MLAgents.CommunicatorObjects.UnityRLCapabilitiesProto.Parser, new[]{ "BaseRLCapabilities", "ConcatenatedPngObservations", "CompressedChannelMapping" }, null, null, null)
new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.UnityRLCapabilitiesProto), global::Unity.MLAgents.CommunicatorObjects.UnityRLCapabilitiesProto.Parser, new[]{ "BaseRLCapabilities", "ConcatenatedPngObservations", "CompressedChannelMapping", "HybridActions" }, null, null, null)
}));
}
#endregion

baseRLCapabilities_ = other.baseRLCapabilities_;
concatenatedPngObservations_ = other.concatenatedPngObservations_;
compressedChannelMapping_ = other.compressedChannelMapping_;
hybridActions_ = other.hybridActions_;
_unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
}

}
}
/// <summary>Field number for the "hybridActions" field.</summary>
public const int HybridActionsFieldNumber = 4;
private bool hybridActions_;
/// <summary>
/// support for hybrid action spaces (discrete + continuous)
/// </summary>
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public bool HybridActions {
get { return hybridActions_; }
set {
hybridActions_ = value;
}
}
[global::System.Diagnostics.DebuggerNonUserCodeAttribute]
public override bool Equals(object other) {
return Equals(other as UnityRLCapabilitiesProto);

if (BaseRLCapabilities != other.BaseRLCapabilities) return false;
if (ConcatenatedPngObservations != other.ConcatenatedPngObservations) return false;
if (CompressedChannelMapping != other.CompressedChannelMapping) return false;
if (HybridActions != other.HybridActions) return false;
return Equals(_unknownFields, other._unknownFields);
}

if (BaseRLCapabilities != false) hash ^= BaseRLCapabilities.GetHashCode();
if (ConcatenatedPngObservations != false) hash ^= ConcatenatedPngObservations.GetHashCode();
if (CompressedChannelMapping != false) hash ^= CompressedChannelMapping.GetHashCode();
if (HybridActions != false) hash ^= HybridActions.GetHashCode();
if (_unknownFields != null) {
hash ^= _unknownFields.GetHashCode();
}

output.WriteRawTag(24);
output.WriteBool(CompressedChannelMapping);
}
if (HybridActions != false) {
output.WriteRawTag(32);
output.WriteBool(HybridActions);
}
if (_unknownFields != null) {
_unknownFields.WriteTo(output);
}

size += 1 + 1;
}
if (CompressedChannelMapping != false) {
size += 1 + 1;
}
if (HybridActions != false) {
size += 1 + 1;
}
if (_unknownFields != null) {

if (other.CompressedChannelMapping != false) {
CompressedChannelMapping = other.CompressedChannelMapping;
}
if (other.HybridActions != false) {
HybridActions = other.HybridActions;
}
_unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
}

}
case 24: {
CompressedChannelMapping = input.ReadBool();
break;
}
case 32: {
HybridActions = input.ReadBool();
break;
}
}

44
com.unity.ml-agents/Runtime/Inference/ApplierImpl.cs


using System.Collections.Generic;
using System.Linq;
using Unity.MLAgents.Inference.Utils;
using Unity.MLAgents.Actuators;
using Unity.Barracuda;
using UnityEngine;

/// </summary>
internal class ContinuousActionOutputApplier : TensorApplier.IApplier
{
public void Apply(TensorProxy tensorProxy, IEnumerable<int> actionIds, Dictionary<int, float[]> lastActions)
readonly ActionSpec m_ActionSpec;
public ContinuousActionOutputApplier(ActionSpec actionSpec)
{
m_ActionSpec = actionSpec;
}
public void Apply(TensorProxy tensorProxy, IEnumerable<int> actionIds, Dictionary<int, ActionBuffers> lastActions)
{
var actionSize = tensorProxy.shape[tensorProxy.shape.Length - 1];
var agentIndex = 0;

{
var actionValue = lastActions[agentId];
if (actionValue == null)
var actionBuffer = lastActions[agentId];
if (actionBuffer.IsEmpty())
actionValue = new float[actionSize];
lastActions[agentId] = actionValue;
actionBuffer = new ActionBuffers(m_ActionSpec);
lastActions[agentId] = actionBuffer;
var continuousBuffer = actionBuffer.ContinuousActions;
actionValue[j] = tensorProxy.data[agentIndex, j];
continuousBuffer[j] = tensorProxy.data[agentIndex, j];
}
}
agentIndex++;

readonly int[] m_ActionSize;
readonly Multinomial m_Multinomial;
readonly ITensorAllocator m_Allocator;
readonly ActionSpec m_ActionSpec;
public DiscreteActionOutputApplier(int[] actionSize, int seed, ITensorAllocator allocator)
public DiscreteActionOutputApplier(ActionSpec actionSpec, int seed, ITensorAllocator allocator)
m_ActionSize = actionSize;
m_ActionSize = actionSpec.BranchSizes;
m_ActionSpec = actionSpec;
public void Apply(TensorProxy tensorProxy, IEnumerable<int> actionIds, Dictionary<int, float[]> lastActions)
public void Apply(TensorProxy tensorProxy, IEnumerable<int> actionIds, Dictionary<int, ActionBuffers> lastActions)
{
//var tensorDataProbabilities = tensorProxy.Data as float[,];
var idActionPairList = actionIds as List<int> ?? actionIds.ToList();

{
if (lastActions.ContainsKey(agentId))
{
var actionVal = lastActions[agentId];
if (actionVal == null)
var actionBuffer = lastActions[agentId];
if (actionBuffer.IsEmpty())
actionVal = new float[m_ActionSize.Length];
lastActions[agentId] = actionVal;
actionBuffer = new ActionBuffers(m_ActionSpec);
lastActions[agentId] = actionBuffer;
var discreteBuffer = actionBuffer.DiscreteActions;
actionVal[j] = actionValues[agentIndex, j];
discreteBuffer[j] = (int)actionValues[agentIndex, j];
}
}
agentIndex++;

m_Memories = memories;
}
public void Apply(TensorProxy tensorProxy, IEnumerable<int> actionIds, Dictionary<int, float[]> lastActions)
public void Apply(TensorProxy tensorProxy, IEnumerable<int> actionIds, Dictionary<int, ActionBuffers> lastActions)
{
var agentIndex = 0;
var memorySize = (int)tensorProxy.shape[tensorProxy.shape.Length - 1];

m_Memories = memories;
}
public void Apply(TensorProxy tensorProxy, IEnumerable<int> actionIds, Dictionary<int, float[]> lastActions)
public void Apply(TensorProxy tensorProxy, IEnumerable<int> actionIds, Dictionary<int, ActionBuffers> lastActions)
{
var agentIndex = 0;
var memorySize = (int)tensorProxy.shape[tensorProxy.shape.Length - 1];

237
com.unity.ml-agents/Runtime/Inference/BarracudaModelParamLoader.cs


/// </summary>
internal class BarracudaModelParamLoader
{
enum ModelActionType
{
Unknown,
Discrete,
Continuous
}
/// Generates the Tensor inputs that are expected to be present in the Model.
/// </summary>
/// <param name="model">
/// The Barracuda engine model for loading static parameters.
/// </param>
/// <returns>TensorProxy IEnumerable with the expected Tensor inputs.</returns>
public static IReadOnlyList<TensorProxy> GetInputTensors(Model model)
{
var tensors = new List<TensorProxy>();
if (model == null)
return tensors;
foreach (var input in model.inputs)
{
tensors.Add(new TensorProxy
{
name = input.name,
valueType = TensorProxy.TensorType.FloatingPoint,
data = null,
shape = input.shape.Select(i => (long)i).ToArray()
});
}
foreach (var mem in model.memories)
{
tensors.Add(new TensorProxy
{
name = mem.input,
valueType = TensorProxy.TensorType.FloatingPoint,
data = null,
shape = TensorUtils.TensorShapeFromBarracuda(mem.shape)
});
}
tensors.Sort((el1, el2) => el1.name.CompareTo(el2.name));
return tensors;
}
public static int GetNumVisualInputs(Model model)
{
var count = 0;
if (model == null)
return count;
foreach (var input in model.inputs)
{
if (input.shape.Length == 4)
{
if (input.name.StartsWith(TensorNames.VisualObservationPlaceholderPrefix))
{
count++;
}
}
}
return count;
}
/// <summary>
/// Generates the Tensor outputs that are expected to be present in the Model.
/// </summary>
/// <param name="model">
/// The Barracuda engine model for loading static parameters
/// </param>
/// <returns>TensorProxy IEnumerable with the expected Tensor outputs</returns>
public static string[] GetOutputNames(Model model)
{
var names = new List<string>();
if (model == null)
{
return names.ToArray();
}
names.Add(TensorNames.ActionOutput);
var memory = (int)model.GetTensorByName(TensorNames.MemorySize)[0];
if (memory > 0)
{
foreach (var mem in model.memories)
{
names.Add(mem.output);
}
}
names.Sort();
return names.ToArray();
}
/// <summary>
/// Factory for the ModelParamLoader : Creates a ModelParamLoader and runs the checks
/// on it.
/// </summary>

return failedModelChecks;
}
foreach (var constantName in TensorNames.RequiredConstants)
var hasExpectedTensors = model.CheckExpectedTensors(failedModelChecks);
if (!hasExpectedTensors)
var tensor = model.GetTensorByName(constantName);
if (tensor == null)
{
failedModelChecks.Add($"Required constant \"{constantName}\" was not found in the model file.");
return failedModelChecks;
}
return failedModelChecks;
var memorySize = (int)model.GetTensorByName(TensorNames.MemorySize)[0];
var isContinuousInt = (int)model.GetTensorByName(TensorNames.IsContinuousControl)[0];
var isContinuous = GetActionType(isContinuousInt);
var actionSize = (int)model.GetTensorByName(TensorNames.ActionOutputShape)[0];
if (modelApiVersion == -1)
{
failedModelChecks.Add(

return failedModelChecks;
}
var modelDiscreteActionSize = isContinuous == ModelActionType.Discrete ? actionSize : 0;
var modelContinuousActionSize = isContinuous == ModelActionType.Continuous ? actionSize : 0;
var memorySize = (int)model.GetTensorByName(TensorNames.MemorySize)[0];
if (memorySize == -1)
{
failedModelChecks.Add($"Missing node in the model provided : {TensorNames.MemorySize}");
return failedModelChecks;
}
CheckIntScalarPresenceHelper(new Dictionary<string, int>()
{
{TensorNames.MemorySize, memorySize},
{TensorNames.IsContinuousControl, isContinuousInt},
{TensorNames.ActionOutputShape, actionSize}
})
CheckInputTensorPresence(model, brainParameters, memorySize, sensorComponents)
CheckInputTensorPresence(model, brainParameters, memorySize, isContinuous, sensorComponents)
CheckOutputTensorPresence(model, memorySize)
failedModelChecks.AddRange(
CheckOutputTensorPresence(model, memorySize))
;
CheckOutputTensorShape(model, brainParameters, actuatorComponents, isContinuous, modelContinuousActionSize, modelDiscreteActionSize)
CheckOutputTensorShape(model, brainParameters, actuatorComponents)
/// Converts the integer value in the model corresponding to the type of control to a
/// ModelActionType.
/// </summary>
/// <param name="isContinuousInt">
/// The integer value in the model indicating the type of control
/// </param>
/// <returns>The equivalent ModelActionType</returns>
static ModelActionType GetActionType(int isContinuousInt)
{
ModelActionType isContinuous;
switch (isContinuousInt)
{
case 0:
isContinuous = ModelActionType.Discrete;
break;
case 1:
isContinuous = ModelActionType.Continuous;
break;
default:
isContinuous = ModelActionType.Unknown;
break;
}
return isContinuous;
}
/// <summary>
/// Given a Dictionary of node names to int values, create checks if the values have the
/// invalid value of -1.
/// </summary>
/// <param name="requiredScalarFields"> Mapping from node names to int values</param>
/// <returns>The list the error messages of the checks that failed</returns>
static IEnumerable<string> CheckIntScalarPresenceHelper(
Dictionary<string, int> requiredScalarFields)
{
var failedModelChecks = new List<string>();
foreach (var field in requiredScalarFields)
{
if (field.Value == -1)
{
failedModelChecks.Add($"Missing node in the model provided : {field.Key}");
}
}
return failedModelChecks;
}
/// <summary>
/// Generates failed checks that correspond to inputs expected by the model that are not
/// present in the BrainParameters.
/// </summary>

Model model,
BrainParameters brainParameters,
int memory,
ModelActionType isContinuous,
var tensorsNames = GetInputTensors(model).Select(x => x.name).ToList();
var tensorsNames = model.GetInputNames();
// If there is no Vector Observation Input but the Brain Parameters expect one.
if ((brainParameters.VectorObservationSize != 0) &&

"The model does not contain a Vector Observation Placeholder Input. " +
"The model does not contain a Vector Observation Placeholder Input. " +
"You must set the Vector Observation Space Size to 0.");
}

visObsIndex++;
}
var expectedVisualObs = GetNumVisualInputs(model);
var expectedVisualObs = model.GetNumVisualInputs();
// Check if there's not enough visual sensors (too many would be handled above)
if (expectedVisualObs > visObsIndex)
{

}
// If the model uses discrete control but does not have an input for action masks
if (isContinuous == ModelActionType.Discrete)
if (model.HasDiscreteOutputs())
{
if (!tensorsNames.Contains(TensorNames.ActionMaskPlaceholder))
{

static IEnumerable<string> CheckOutputTensorPresence(Model model, int memory)
{
var failedModelChecks = new List<string>();
// If there is no Action Output.
if (!model.outputs.Contains(TensorNames.ActionOutput))
{
failedModelChecks.Add("The model does not contain an Action Output Node.");
}
// If there is no Recurrent Output but the model is Recurrent.
if (memory > 0)

}
// If the model expects an input but it is not in this list
foreach (var tensor in GetInputTensors(model))
foreach (var tensor in model.GetInputTensors())
{
if (!tensorTester.ContainsKey(tensor.name))
{

BrainParameters brainParameters, TensorProxy tensorProxy,
SensorComponent[] sensorComponents, int observableAttributeTotalSize)
{
// TODO: Update this check after intergrating ActionSpec into BrainParameters
var numberActionsBp = brainParameters.VectorActionSize.Length;
var numberActionsT = tensorProxy.shape[tensorProxy.shape.Length - 1];
if (numberActionsBp != numberActionsT)

static IEnumerable<string> CheckOutputTensorShape(
Model model,
BrainParameters brainParameters,
ActuatorComponent[] actuatorComponents,
ModelActionType isContinuous,
int modelContinuousActionSize, int modelSumDiscreteBranchSizes)
ActuatorComponent[] actuatorComponents)
if (isContinuous == ModelActionType.Unknown)
{
failedModelChecks.Add("Cannot infer type of Control from the provided model.");
return failedModelChecks;
}
if (isContinuous == ModelActionType.Continuous &&
brainParameters.VectorActionSpaceType != SpaceType.Continuous)
{
failedModelChecks.Add(
"Model has been trained using Continuous Control but the Brain Parameters " +
"suggest Discrete Control.");
return failedModelChecks;
}
if (isContinuous == ModelActionType.Discrete &&
brainParameters.VectorActionSpaceType != SpaceType.Discrete)
{
failedModelChecks.Add(
"Model has been trained using Discrete Control but the Brain Parameters " +
"suggest Continuous Control.");
return failedModelChecks;
}
// This will need to change a bit for hybrid action spaces.
if (isContinuous == ModelActionType.Continuous)
if (model.HasContinuousOutputs())
tensorTester[TensorNames.ActionOutput] = CheckContinuousActionOutputShape;
tensorTester[model.ContinuousOutputName()] = CheckContinuousActionOutputShape;
else
if (model.HasDiscreteOutputs())
tensorTester[TensorNames.ActionOutput] = CheckDiscreteActionOutputShape;
tensorTester[model.DiscreteOutputName()] = CheckDiscreteActionOutputShape;
var modelContinuousActionSize = model.ContinuousOutputSize();
var modelSumDiscreteBranchSizes = model.DiscreteOutputSize();
foreach (var name in model.outputs)
{
if (tensorTester.ContainsKey(name))

4
com.unity.ml-agents/Runtime/Inference/GeneratorImpl.cs


foreach (var infoSensorPair in infos)
{
var info = infoSensorPair.agentInfo;
var pastAction = info.storedVectorActions;
if (pastAction != null)
var pastAction = info.storedVectorActions.DiscreteActions;
if (!pastAction.IsEmpty())
{
for (var j = 0; j < actionSize; j++)
{

12
com.unity.ml-agents/Runtime/Inference/ModelRunner.cs


internal class ModelRunner
{
List<AgentInfoSensorsPair> m_Infos = new List<AgentInfoSensorsPair>();
Dictionary<int, float[]> m_LastActionsReceived = new Dictionary<int, float[]>();
Dictionary<int, ActionBuffers> m_LastActionsReceived = new Dictionary<int, ActionBuffers>();
List<int> m_OrderedAgentsRequestingDecisions = new List<int>();
ITensorAllocator m_TensorAllocator;

m_Engine = null;
}
m_InferenceInputs = BarracudaModelParamLoader.GetInputTensors(barracudaModel);
m_OutputNames = BarracudaModelParamLoader.GetOutputNames(barracudaModel);
m_InferenceInputs = barracudaModel.GetInputTensors();
m_OutputNames = barracudaModel.GetOutputNames();
m_TensorGenerator = new TensorGenerator(
seed, m_TensorAllocator, m_Memories, barracudaModel);
m_TensorApplier = new TensorApplier(

if (!m_LastActionsReceived.ContainsKey(info.episodeId))
{
m_LastActionsReceived[info.episodeId] = null;
m_LastActionsReceived[info.episodeId] = ActionBuffers.Empty;
}
if (info.done)
{

return m_Model == other && m_InferenceDevice == otherInferenceDevice;
}
public float[] GetAction(int agentId)
public ActionBuffers GetAction(int agentId)
return null;
return ActionBuffers.Empty;
}
}
}

35
com.unity.ml-agents/Runtime/Inference/TensorApplier.cs


/// </param>
/// <param name="actionIds"> List of Agents Ids that will be updated using the tensor's data</param>
/// <param name="lastActions"> Dictionary of AgentId to Actions to be updated</param>
void Apply(TensorProxy tensorProxy, IEnumerable<int> actionIds, Dictionary<int, float[]> lastActions);
void Apply(TensorProxy tensorProxy, IEnumerable<int> actionIds, Dictionary<int, ActionBuffers> lastActions);
}
readonly Dictionary<string, IApplier> m_Dict = new Dictionary<string, IApplier>();

Dictionary<int, List<float>> memories,
object barracudaModel = null)
{
actionSpec.CheckNotHybrid();
// If model is null, no inference to run and exception is thrown before reaching here.
if (barracudaModel == null)
{
return;
}
var model = (Model)barracudaModel;
if (!model.HasHybridSupport())
{
actionSpec.CheckNotHybrid();
}
m_Dict[TensorNames.ActionOutput] = new ContinuousActionOutputApplier();
var tensorName = model.ContinuousOutputName();
m_Dict[tensorName] = new ContinuousActionOutputApplier(actionSpec);
else
if (actionSpec.NumDiscreteActions > 0)
m_Dict[TensorNames.ActionOutput] =
new DiscreteActionOutputApplier(actionSpec.BranchSizes, seed, allocator);
var tensorName = model.DiscreteOutputName();
m_Dict[tensorName] = new DiscreteActionOutputApplier(actionSpec, seed, allocator);
if (barracudaModel != null)
for (var i = 0; i < model?.memories.Count; i++)
var model = (Model)barracudaModel;
for (var i = 0; i < model?.memories.Count; i++)
{
m_Dict[model.memories[i].output] =
new BarracudaMemoryOutputApplier(model.memories.Count, i, memories);
}
m_Dict[model.memories[i].output] =
new BarracudaMemoryOutputApplier(model.memories.Count, i, memories);
}
}

/// <exception cref="UnityAgentsException"> One of the tensor does not have an
/// associated applier.</exception>
public void ApplyTensors(
IEnumerable<TensorProxy> tensors, IEnumerable<int> actionIds, Dictionary<int, float[]> lastActions)
IEnumerable<TensorProxy> tensors, IEnumerable<int> actionIds, Dictionary<int, ActionBuffers> lastActions)
{
foreach (var tensor in tensors)
{

26
com.unity.ml-agents/Runtime/Inference/TensorGenerator.cs


Dictionary<int, List<float>> memories,
object barracudaModel = null)
{
// If model is null, no inference to run and exception is thrown before reaching here.
if (barracudaModel == null)
{
return;
}
var model = (Model)barracudaModel;
// Generator for Inputs
m_Dict[TensorNames.BatchSizePlaceholder] =
new BatchSizeGenerator(allocator);

new RecurrentInputGenerator(allocator, memories);
if (barracudaModel != null)
for (var i = 0; i < model.memories.Count; i++)
var model = (Model)barracudaModel;
for (var i = 0; i < model.memories.Count; i++)
{
m_Dict[model.memories[i].input] =
new BarracudaRecurrentInputGenerator(i, allocator, memories);
}
m_Dict[model.memories[i].input] =
new BarracudaRecurrentInputGenerator(i, allocator, memories);
}
m_Dict[TensorNames.PreviousActionPlaceholder] =

// Generators for Outputs
m_Dict[TensorNames.ActionOutput] = new BiDimensionalOutputGenerator(allocator);
if (model.HasContinuousOutputs())
{
m_Dict[model.ContinuousOutputName()] = new BiDimensionalOutputGenerator(allocator);
}
if (model.HasDiscreteOutputs())
{
m_Dict[model.DiscreteOutputName()] = new BiDimensionalOutputGenerator(allocator);
}
m_Dict[TensorNames.RecurrentOutput] = new BiDimensionalOutputGenerator(allocator);
m_Dict[TensorNames.ValueEstimateOutput] = new BiDimensionalOutputGenerator(allocator);
}

15
com.unity.ml-agents/Runtime/Inference/TensorNames.cs


public const string recurrentOutputC = "recurrent_out_c";
public const string MemorySize = "memory_size";
public const string VersionNumber = "version_number";
public const string IsContinuousControl = "is_continuous_control";
public const string ActionOutputShape = "action_output_shape";
public const string ActionOutput = "action";
public const string ContinuousActionOutputShape = "continuous_action_output_shape";
public const string DiscreteActionOutputShape = "discrete_action_output_shape";
public const string ContinuousActionOutput = "continuous_actions";
public const string DiscreteActionOutput = "discrete_actions";
public static readonly string[] RequiredConstants =
{
VersionNumber, MemorySize, IsContinuousControl, ActionOutputShape
};
// Deprecated TensorNames entries for backward compatibility
public const string IsContinuousControlDeprecated = "is_continuous_control";
public const string ActionOutputDeprecated = "action";
public const string ActionOutputShapeDeprecated = "action_output_shape";
}
}

20
com.unity.ml-agents/Runtime/Policies/BarracudaPolicy.cs


/// Sensor shapes for the associated Agents. All Agents must have the same shapes for their Sensors.
/// </summary>
List<int[]> m_SensorShapes;
SpaceType m_SpaceType;
ActionSpec m_ActionSpec;
/// <inheritdoc />
public BarracudaPolicy(

{
var modelRunner = Academy.Instance.GetOrCreateModelRunner(model, actionSpec, inferenceDevice);
m_ModelRunner = modelRunner;
actionSpec.CheckNotHybrid();
m_SpaceType = actionSpec.NumContinuousActions > 0 ? SpaceType.Continuous : SpaceType.Discrete;
m_ActionSpec = actionSpec;
}
/// <inheritdoc />

/// <inheritdoc />
public ref readonly ActionBuffers DecideAction()
{
m_ModelRunner?.DecideBatch();
var actions = m_ModelRunner?.GetAction(m_AgentId);
if (m_SpaceType == SpaceType.Continuous)
if (m_ModelRunner == null)
m_LastActionBuffer = new ActionBuffers(actions, Array.Empty<int>());
return ref m_LastActionBuffer;
m_LastActionBuffer = ActionBuffers.Empty;
m_LastActionBuffer = ActionBuffers.FromDiscreteActions(actions);
else
{
m_ModelRunner?.DecideBatch();
m_LastActionBuffer = m_ModelRunner.GetAction(m_AgentId);
}
return ref m_LastActionBuffer;
}

14
com.unity.ml-agents/Runtime/Policies/RemotePolicy.cs


{
int m_AgentId;
string m_FullyQualifiedBehaviorName;
SpaceType m_SpaceType;
ActionSpec m_ActionSpec;
ActionBuffers m_LastActionBuffer;
internal ICommunicator m_Communicator;

m_FullyQualifiedBehaviorName = fullyQualifiedBehaviorName;
m_Communicator = Academy.Instance.Communicator;
m_Communicator.SubscribeBrain(m_FullyQualifiedBehaviorName, actionSpec);
actionSpec.CheckNotHybrid();
m_SpaceType = actionSpec.NumContinuousActions > 0 ? SpaceType.Continuous : SpaceType.Discrete;
m_ActionSpec = actionSpec;
}
/// <inheritdoc />

{
m_Communicator?.DecideBatch();
var actions = m_Communicator?.GetActions(m_FullyQualifiedBehaviorName, m_AgentId);
// TODO figure out how to handle this with multiple space types.
if (m_SpaceType == SpaceType.Continuous)
{
m_LastActionBuffer = new ActionBuffers(actions, Array.Empty<int>());
return ref m_LastActionBuffer;
}
m_LastActionBuffer = ActionBuffers.FromDiscreteActions(actions);
m_LastActionBuffer = actions == null ? ActionBuffers.Empty : (ActionBuffers)actions;
return ref m_LastActionBuffer;
}

12
com.unity.ml-agents/Tests/Editor/Actuators/ActuatorManagerTests.cs


}
[Test]
public void TestFailOnMixedActionSpace()
{
var manager = new ActuatorManager();
var actuator1 = new TestActuator(ActionSpec.MakeDiscrete(new[] { 1, 2, 3, 4 }), "actuator1");
var actuator2 = new TestActuator(ActionSpec.MakeContinuous(3), "actuator2");
manager.Add(actuator1);
manager.Add(actuator2);
LogAssert.Expect(LogType.Assert, "Actuators on the same Agent must have the same action SpaceType.");
manager.ReadyActuatorsForExecution(new[] { actuator1, actuator2 }, 3, 10, 4);
}
[Test]
public void TestFailOnSameActuatorName()
{
var manager = new ActuatorManager();

3
com.unity.ml-agents/Tests/Editor/DemonstrationTests.cs


using UnityEngine;
using System.IO.Abstractions.TestingHelpers;
using System.Reflection;
using Unity.MLAgents.Actuators;
using Unity.MLAgents.CommunicatorObjects;
using Unity.MLAgents.Sensors;
using Unity.MLAgents.Demonstrations;

done = true,
episodeId = 5,
maxStepReached = true,
storedVectorActions = new[] { 0f, 1f },
storedVectorActions = new ActionBuffers(null, new int[] { 0, 1 }),
};

74
com.unity.ml-agents/Tests/Editor/EditModeTestInternalBrainTensorApplier.cs


using Unity.Barracuda;
using Unity.MLAgents.Actuators;
using Unity.MLAgents.Inference;
using Unity.MLAgents.Policies;
namespace Unity.MLAgents.Tests
{

[Test]
public void ApplyContinuousActionOutput()
{
var actionSpec = ActionSpec.MakeContinuous(3);
var inputTensor = new TensorProxy()
{
shape = new long[] { 2, 3 },

var applier = new ContinuousActionOutputApplier();
var applier = new ContinuousActionOutputApplier(actionSpec);
var actionDict = new Dictionary<int, float[]>() { { 0, null }, { 1, null } };
var actionDict = new Dictionary<int, ActionBuffers>() { { 0, ActionBuffers.Empty }, { 1, ActionBuffers.Empty } };
Assert.AreEqual(actionDict[0][0], 1);
Assert.AreEqual(actionDict[0][1], 2);
Assert.AreEqual(actionDict[0][2], 3);
Assert.AreEqual(actionDict[0].ContinuousActions[0], 1);
Assert.AreEqual(actionDict[0].ContinuousActions[1], 2);
Assert.AreEqual(actionDict[0].ContinuousActions[2], 3);
Assert.AreEqual(actionDict[1][0], 4);
Assert.AreEqual(actionDict[1][1], 5);
Assert.AreEqual(actionDict[1][2], 6);
Assert.AreEqual(actionDict[1].ContinuousActions[0], 4);
Assert.AreEqual(actionDict[1].ContinuousActions[1], 5);
Assert.AreEqual(actionDict[1].ContinuousActions[2], 6);
var actionSpec = ActionSpec.MakeDiscrete(new int[] { 2, 3 });
var inputTensor = new TensorProxy()
{
shape = new long[] { 2, 5 },

new[] { 0.5f, 22.5f, 0.1f, 5f, 1f, 4f, 5f, 6f, 7f, 8f })
};
var alloc = new TensorCachingAllocator();
var applier = new DiscreteActionOutputApplier(new[] { 2, 3 }, 0, alloc);
var applier = new DiscreteActionOutputApplier(actionSpec, 0, alloc);
var actionDict = new Dictionary<int, float[]>() { { 0, null }, { 1, null } };
var actionDict = new Dictionary<int, ActionBuffers>() { { 0, ActionBuffers.Empty }, { 1, ActionBuffers.Empty } };
Assert.AreEqual(actionDict[0][0], 1);
Assert.AreEqual(actionDict[0][1], 1);
Assert.AreEqual(actionDict[0].DiscreteActions[0], 1);
Assert.AreEqual(actionDict[0].DiscreteActions[1], 1);
Assert.AreEqual(actionDict[1][0], 1);
Assert.AreEqual(actionDict[1][1], 2);
Assert.AreEqual(actionDict[1].DiscreteActions[0], 1);
Assert.AreEqual(actionDict[1].DiscreteActions[1], 2);
alloc.Dispose();
}
[Test]
public void ApplyHybridActionOutput()
{
var actionSpec = new ActionSpec(3, 2, new int[] { 2, 3 });
var continuousInputTensor = new TensorProxy()
{
shape = new long[] { 2, 3 },
data = new Tensor(2, 3, new float[] { 1, 2, 3, 4, 5, 6 })
};
var discreteInputTensor = new TensorProxy()
{
shape = new long[] { 2, 8 },
data = new Tensor(
2,
5,
new[] { 0.5f, 22.5f, 0.1f, 5f, 1f, 4f, 5f, 6f, 7f, 8f })
};
var continuousApplier = new ContinuousActionOutputApplier(actionSpec);
var alloc = new TensorCachingAllocator();
var discreteApplier = new DiscreteActionOutputApplier(actionSpec, 0, alloc);
var agentIds = new List<int>() { 0, 1 };
// Dictionary from AgentId to Action
var actionDict = new Dictionary<int, ActionBuffers>() { { 0, ActionBuffers.Empty }, { 1, ActionBuffers.Empty } };
continuousApplier.Apply(continuousInputTensor, agentIds, actionDict);
discreteApplier.Apply(discreteInputTensor, agentIds, actionDict);
Assert.AreEqual(actionDict[0].ContinuousActions[0], 1);
Assert.AreEqual(actionDict[0].ContinuousActions[1], 2);
Assert.AreEqual(actionDict[0].ContinuousActions[2], 3);
Assert.AreEqual(actionDict[0].DiscreteActions[0], 1);
Assert.AreEqual(actionDict[0].DiscreteActions[1], 1);
Assert.AreEqual(actionDict[1].ContinuousActions[0], 4);
Assert.AreEqual(actionDict[1].ContinuousActions[1], 5);
Assert.AreEqual(actionDict[1].ContinuousActions[2], 6);
Assert.AreEqual(actionDict[1].DiscreteActions[0], 1);
Assert.AreEqual(actionDict[1].DiscreteActions[1], 2);
alloc.Dispose();
}
}

7
com.unity.ml-agents/Tests/Editor/EditModeTestInternalBrainTensorGenerator.cs


using Unity.Barracuda;
using NUnit.Framework;
using UnityEngine;
using Unity.MLAgents.Actuators;
using Unity.MLAgents.Inference;
using Unity.MLAgents.Policies;
using Unity.MLAgents.Sensors.Reflection;

var infoA = new AgentInfo
{
storedVectorActions = new[] { 1f, 2f },
discreteActionMasks = null
storedVectorActions = new ActionBuffers(null, new[] { 1, 2 }),
discreteActionMasks = null,
storedVectorActions = new[] { 3f, 4f },
storedVectorActions = new ActionBuffers(null, new[] { 3, 4 }),
discreteActionMasks = new[] { true, false, false, false, false },
};

62
com.unity.ml-agents/Tests/Editor/ModelRunnerTest.cs


using Unity.Barracuda;
using Unity.MLAgents.Actuators;
using Unity.MLAgents.Inference;
using Unity.MLAgents.Sensors;
using Unity.MLAgents.Policies;
namespace Unity.MLAgents.Tests

{
const string k_continuous2vis8vec2actionPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action.nn";
const string k_discrete1vis0vec_2_3action_recurrModelPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_recurr.nn";
NNModel continuous2vis8vec2actionModel;
NNModel discrete1vis0vec_2_3action_recurrModel;
const string k_continuousONNXPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action.onnx";
const string k_discreteONNXPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_recurr.onnx";
const string k_hybridONNXPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/hybrid0vis53vec_3c_2daction.onnx";
const string k_continuousNNPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action_deprecated.nn";
const string k_discreteNNPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_recurr_deprecated.nn";
NNModel continuousONNXModel;
NNModel discreteONNXModel;
NNModel hybridONNXModel;
NNModel continuousNNModel;
NNModel discreteNNModel;
Test3DSensorComponent sensor_21_20_3;
Test3DSensorComponent sensor_20_22_3;

return ActionSpec.MakeDiscrete(2, 3);
}
ActionSpec GetHybrid0vis53vec_3c_2dActionSpec()
{
return new ActionSpec(3, 1, new int[] { 2 });
}
continuous2vis8vec2actionModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_continuous2vis8vec2actionPath, typeof(NNModel));
discrete1vis0vec_2_3action_recurrModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_discrete1vis0vec_2_3action_recurrModelPath, typeof(NNModel));
continuousONNXModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_continuousONNXPath, typeof(NNModel));
discreteONNXModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_discreteONNXPath, typeof(NNModel));
hybridONNXModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_hybridONNXPath, typeof(NNModel));
continuousNNModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_continuousNNPath, typeof(NNModel));
discreteNNModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_discreteNNPath, typeof(NNModel));
var go = new GameObject("SensorA");
sensor_21_20_3 = go.AddComponent<Test3DSensorComponent>();
sensor_21_20_3.Sensor = new Test3DSensor("SensorA", 21, 20, 3);

[Test]
public void TestModelExist()
{
Assert.IsNotNull(continuous2vis8vec2actionModel);
Assert.IsNotNull(discrete1vis0vec_2_3action_recurrModel);
Assert.IsNotNull(continuousONNXModel);
Assert.IsNotNull(discreteONNXModel);
Assert.IsNotNull(hybridONNXModel);
Assert.IsNotNull(continuousNNModel);
Assert.IsNotNull(discreteNNModel);
var modelRunner = new ModelRunner(continuous2vis8vec2actionModel, GetContinuous2vis8vec2actionActionSpec());
var modelRunner = new ModelRunner(continuousONNXModel, GetContinuous2vis8vec2actionActionSpec());
modelRunner = new ModelRunner(discrete1vis0vec_2_3action_recurrModel, GetDiscrete1vis0vec_2_3action_recurrModelActionSpec());
modelRunner = new ModelRunner(discreteONNXModel, GetDiscrete1vis0vec_2_3action_recurrModelActionSpec());
modelRunner.Dispose();
modelRunner = new ModelRunner(hybridONNXModel, GetHybrid0vis53vec_3c_2dActionSpec());
modelRunner.Dispose();
modelRunner = new ModelRunner(continuousNNModel, GetContinuous2vis8vec2actionActionSpec());
modelRunner.Dispose();
modelRunner = new ModelRunner(discreteNNModel, GetDiscrete1vis0vec_2_3action_recurrModelActionSpec());
modelRunner.Dispose();
}

var modelRunner = new ModelRunner(continuous2vis8vec2actionModel, GetContinuous2vis8vec2actionActionSpec(), InferenceDevice.CPU);
Assert.True(modelRunner.HasModel(continuous2vis8vec2actionModel, InferenceDevice.CPU));
Assert.False(modelRunner.HasModel(continuous2vis8vec2actionModel, InferenceDevice.GPU));
Assert.False(modelRunner.HasModel(discrete1vis0vec_2_3action_recurrModel, InferenceDevice.CPU));
var modelRunner = new ModelRunner(continuousONNXModel, GetContinuous2vis8vec2actionActionSpec(), InferenceDevice.CPU);
Assert.True(modelRunner.HasModel(continuousONNXModel, InferenceDevice.CPU));
Assert.False(modelRunner.HasModel(continuousONNXModel, InferenceDevice.GPU));
Assert.False(modelRunner.HasModel(discreteONNXModel, InferenceDevice.CPU));
modelRunner.Dispose();
}

var actionSpec = GetDiscrete1vis0vec_2_3action_recurrModelActionSpec();
var modelRunner = new ModelRunner(discrete1vis0vec_2_3action_recurrModel, actionSpec);
var modelRunner = new ModelRunner(discreteONNXModel, actionSpec);
var info1 = new AgentInfo();
info1.episodeId = 1;
modelRunner.PutObservations(info1, new[] { sensor_21_20_3.CreateSensor() }.ToList());

modelRunner.DecideBatch();
Assert.IsNotNull(modelRunner.GetAction(1));
Assert.IsNotNull(modelRunner.GetAction(2));
Assert.IsNull(modelRunner.GetAction(3));
Assert.AreEqual(actionSpec.NumDiscreteActions, modelRunner.GetAction(1).Count());
Assert.IsFalse(modelRunner.GetAction(1).Equals(ActionBuffers.Empty));
Assert.IsFalse(modelRunner.GetAction(2).Equals(ActionBuffers.Empty));
Assert.IsTrue(modelRunner.GetAction(3).Equals(ActionBuffers.Empty));
Assert.AreEqual(actionSpec.NumDiscreteActions, modelRunner.GetAction(1).DiscreteActions.Length);
modelRunner.Dispose();
}
}

212
com.unity.ml-agents/Tests/Editor/ParameterLoaderTest.cs


[TestFixture]
public class ParameterLoaderTest
{
const string k_continuous2vis8vec2actionPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action.nn";
const string k_discrete1vis0vec_2_3action_recurrModelPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_recurr.nn";
NNModel continuous2vis8vec2actionModel;
NNModel discrete1vis0vec_2_3action_recurrModel;
// ONNX model with continuous/discrete action output (support hybrid action)
const string k_continuousONNXPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action.onnx";
const string k_discreteONNXPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_recurr.onnx";
const string k_hybridONNXPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/hybrid0vis53vec_3c_2daction.onnx";
// NN model with single action output (deprecated, does not support hybrid action).
// Same BrainParameters settings as the corresponding ONNX model.
const string k_continuousNNPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action_deprecated.nn";
const string k_discreteNNPath = "Packages/com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_recurr_deprecated.nn";
NNModel continuousONNXModel;
NNModel discreteONNXModel;
NNModel hybridONNXModel;
NNModel continuousNNModel;
NNModel discreteNNModel;
Test3DSensorComponent sensor_21_20_3;
Test3DSensorComponent sensor_20_22_3;

return validBrainParameters;
}
// TODO: update and enable this after integrating action spec into BrainParameters
// BrainParameters GetHybridBrainParameters()
// {
// var validBrainParameters = new BrainParameters();
// validBrainParameters.VectorObservationSize = 53;
// validBrainParameters.VectorActionSize = new[] { 2 };
// validBrainParameters.NumStackedVectorObservations = 1;
// validBrainParameters.VectorActionSpaceType = SpaceType.Discrete;
// return validBrainParameters;
// }
continuous2vis8vec2actionModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_continuous2vis8vec2actionPath, typeof(NNModel));
discrete1vis0vec_2_3action_recurrModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_discrete1vis0vec_2_3action_recurrModelPath, typeof(NNModel));
continuousONNXModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_continuousONNXPath, typeof(NNModel));
discreteONNXModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_discreteONNXPath, typeof(NNModel));
hybridONNXModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_hybridONNXPath, typeof(NNModel));
continuousNNModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_continuousNNPath, typeof(NNModel));
discreteNNModel = (NNModel)AssetDatabase.LoadAssetAtPath(k_discreteNNPath, typeof(NNModel));
var go = new GameObject("SensorA");
sensor_21_20_3 = go.AddComponent<Test3DSensorComponent>();
sensor_21_20_3.Sensor = new Test3DSensor("SensorA", 21, 20, 3);

[Test]
public void TestModelExist()
{
Assert.IsNotNull(continuous2vis8vec2actionModel);
Assert.IsNotNull(discrete1vis0vec_2_3action_recurrModel);
Assert.IsNotNull(continuousONNXModel);
Assert.IsNotNull(discreteONNXModel);
Assert.IsNotNull(hybridONNXModel);
Assert.IsNotNull(continuousNNModel);
Assert.IsNotNull(discreteNNModel);
[Test]
public void TestGetInputTensors1()
[TestCase(true)]
[TestCase(false)]
public void TestGetInputTensorsContinuous(bool useDeprecatedNNModel)
var model = ModelLoader.Load(continuous2vis8vec2actionModel);
var inputTensors = BarracudaModelParamLoader.GetInputTensors(model);
var inputNames = inputTensors.Select(x => x.name).ToList();
var model = useDeprecatedNNModel ? ModelLoader.Load(continuousNNModel) : ModelLoader.Load(continuousONNXModel);
var inputNames = model.GetInputNames();
Assert.AreEqual(3, inputNames.Count);
Assert.AreEqual(3, inputNames.Count());
Assert.AreEqual(2, BarracudaModelParamLoader.GetNumVisualInputs(model));
Assert.AreEqual(2, model.GetNumVisualInputs());
Assert.AreEqual(0, BarracudaModelParamLoader.GetInputTensors(null).Count);
Assert.AreEqual(0, BarracudaModelParamLoader.GetNumVisualInputs(null));
model = null;
Assert.AreEqual(0, model.GetInputTensors().Count);
Assert.AreEqual(0, model.GetNumVisualInputs());
[Test]
public void TestGetInputTensors2()
[TestCase(true)]
[TestCase(false)]
public void TestGetInputTensorsDiscrete(bool useDeprecatedNNModel)
var model = ModelLoader.Load(discrete1vis0vec_2_3action_recurrModel);
var inputTensors = BarracudaModelParamLoader.GetInputTensors(model);
var inputNames = inputTensors.Select(x => x.name).ToList();
var model = useDeprecatedNNModel ? ModelLoader.Load(discreteNNModel) : ModelLoader.Load(discreteONNXModel);
var inputNames = model.GetInputNames();
// Model should contain 2 inputs : recurrent and visual 1
Assert.Contains(TensorNames.VisualObservationPlaceholderPrefix + "0", inputNames);

[Test]
public void TestGetOutputTensors1()
public void TestGetInputTensorsHybrid()
{
var model = ModelLoader.Load(hybridONNXModel);
var inputNames = model.GetInputNames();
Assert.Contains(TensorNames.VectorObservationPlaceholder, inputNames);
}
[TestCase(true)]
[TestCase(false)]
public void TestGetOutputTensorsContinuous(bool useDeprecatedNNModel)
var model = ModelLoader.Load(continuous2vis8vec2actionModel);
var outputNames = BarracudaModelParamLoader.GetOutputNames(model);
Assert.Contains(TensorNames.ActionOutput, outputNames);
var model = useDeprecatedNNModel ? ModelLoader.Load(continuousNNModel) : ModelLoader.Load(continuousONNXModel);
var outputNames = model.GetOutputNames();
var actionOutputName = useDeprecatedNNModel ? TensorNames.ActionOutputDeprecated : TensorNames.ContinuousActionOutput;
Assert.Contains(actionOutputName, outputNames);
Assert.AreEqual(0, BarracudaModelParamLoader.GetOutputNames(null).Count());
model = null;
Assert.AreEqual(0, model.GetOutputNames().Count());
[Test]
public void TestGetOutputTensors2()
[TestCase(true)]
[TestCase(false)]
public void TestGetOutputTensorsDiscrete(bool useDeprecatedNNModel)
var model = ModelLoader.Load(discrete1vis0vec_2_3action_recurrModel);
var outputNames = BarracudaModelParamLoader.GetOutputNames(model);
Assert.Contains(TensorNames.ActionOutput, outputNames);
var model = useDeprecatedNNModel ? ModelLoader.Load(discreteNNModel) : ModelLoader.Load(discreteONNXModel);
var outputNames = model.GetOutputNames();
var actionOutputName = useDeprecatedNNModel ? TensorNames.ActionOutputDeprecated : TensorNames.DiscreteActionOutput;
Assert.Contains(actionOutputName, outputNames);
public void TestCheckModelValid1()
public void TestGetOutputTensorsHybrid()
var model = ModelLoader.Load(continuous2vis8vec2actionModel);
var model = ModelLoader.Load(hybridONNXModel);
var outputNames = model.GetOutputNames();
Assert.AreEqual(2, outputNames.Count());
Assert.Contains(TensorNames.ContinuousActionOutput, outputNames);
Assert.Contains(TensorNames.DiscreteActionOutput, outputNames);
model = null;
Assert.AreEqual(0, model.GetOutputNames().Count());
}
[TestCase(true)]
[TestCase(false)]
public void TestCheckModelValidContinuous(bool useDeprecatedNNModel)
{
var model = useDeprecatedNNModel ? ModelLoader.Load(continuousNNModel) : ModelLoader.Load(continuousONNXModel);
var validBrainParameters = GetContinuous2vis8vec2actionBrainParameters();
var errors = BarracudaModelParamLoader.CheckModel(

Assert.AreEqual(0, errors.Count()); // There should not be any errors
}
[Test]
public void TestCheckModelValid2()
[TestCase(true)]
[TestCase(false)]
public void TestCheckModelValidDiscrete(bool useDeprecatedNNModel)
var model = ModelLoader.Load(discrete1vis0vec_2_3action_recurrModel);
var model = useDeprecatedNNModel ? ModelLoader.Load(discreteNNModel) : ModelLoader.Load(discreteONNXModel);
var validBrainParameters = GetDiscrete1vis0vec_2_3action_recurrModelBrainParameters();
var errors = BarracudaModelParamLoader.CheckModel(

Assert.AreEqual(0, errors.Count()); // There should not be any errors
}
[Test]
public void TestCheckModelThrowsVectorObservation1()
// TODO: update and enable this test after integrating action spec into BrainParameters
// [Test]
// public void TestCheckModelValidHybrid()
// {
// var model = ModelLoader.Load(hybridModel);
// var validBrainParameters = GetHybridBrainParameters();
// var errors = BarracudaModelParamLoader.CheckModel(
// model, validBrainParameters,
// new SensorComponent[] { }, new ActuatorComponent[0]
// );
// Assert.AreEqual(0, errors.Count()); // There should not be any errors
// }
[TestCase(true)]
[TestCase(false)]
public void TestCheckModelThrowsVectorObservationContinuous(bool useDeprecatedNNModel)
var model = ModelLoader.Load(continuous2vis8vec2actionModel);
var model = useDeprecatedNNModel ? ModelLoader.Load(continuousNNModel) : ModelLoader.Load(continuousONNXModel);
var brainParameters = GetContinuous2vis8vec2actionBrainParameters();
brainParameters.VectorObservationSize = 9; // Invalid observation

Assert.Greater(errors.Count(), 0);
}
[Test]
public void TestCheckModelThrowsVectorObservation2()
[TestCase(true)]
[TestCase(false)]
public void TestCheckModelThrowsVectorObservationDiscrete(bool useDeprecatedNNModel)
var model = ModelLoader.Load(discrete1vis0vec_2_3action_recurrModel);
var model = useDeprecatedNNModel ? ModelLoader.Load(discreteNNModel) : ModelLoader.Load(discreteONNXModel);
var brainParameters = GetDiscrete1vis0vec_2_3action_recurrModelBrainParameters();
brainParameters.VectorObservationSize = 1; // Invalid observation

[Test]
public void TestCheckModelThrowsAction1()
// TODO: update and enable this test after integrating action spec into BrainParameters
// [Test]
// public void TestCheckModelThrowsVectorObservationHybrid()
// {
// var model = ModelLoader.Load(hybridModel);
// var brainParameters = GetHybridBrainParameters();
// brainParameters.VectorObservationSize = 9; // Invalid observation
// var errors = BarracudaModelParamLoader.CheckModel(
// model, brainParameters,
// new SensorComponent[] { }, new ActuatorComponent[0]
// );
// Assert.Greater(errors.Count(), 0);
// brainParameters = GetContinuous2vis8vec2actionBrainParameters();
// brainParameters.NumStackedVectorObservations = 2;// Invalid stacking
// errors = BarracudaModelParamLoader.CheckModel(
// model, brainParameters,
// new SensorComponent[] { }, new ActuatorComponent[0]
// );
// Assert.Greater(errors.Count(), 0);
// }
[TestCase(true)]
[TestCase(false)]
public void TestCheckModelThrowsActionContinuous(bool useDeprecatedNNModel)
var model = ModelLoader.Load(continuous2vis8vec2actionModel);
var model = useDeprecatedNNModel ? ModelLoader.Load(continuousNNModel) : ModelLoader.Load(continuousONNXModel);
var brainParameters = GetContinuous2vis8vec2actionBrainParameters();
brainParameters.VectorActionSize = new[] { 3 }; // Invalid action

Assert.Greater(errors.Count(), 0);
}
[Test]
public void TestCheckModelThrowsAction2()
[TestCase(true)]
[TestCase(false)]
public void TestCheckModelThrowsActionDiscrete(bool useDeprecatedNNModel)
var model = ModelLoader.Load(discrete1vis0vec_2_3action_recurrModel);
var model = useDeprecatedNNModel ? ModelLoader.Load(discreteNNModel) : ModelLoader.Load(discreteONNXModel);
var brainParameters = GetDiscrete1vis0vec_2_3action_recurrModelBrainParameters();
brainParameters.VectorActionSize = new[] { 3, 3 }; // Invalid action

errors = BarracudaModelParamLoader.CheckModel(model, brainParameters, new SensorComponent[] { sensor_21_20_3 }, new ActuatorComponent[0]);
Assert.Greater(errors.Count(), 0);
}
// TODO: update and enable this test after integrating action spec into BrainParameters
// [Test]
// public void TestCheckModelThrowsActionHybrid()
// {
// var model = ModelLoader.Load(hybridModel);
// var brainParameters = GetHybridBrainParameters();
// brainParameters.VectorActionSize = new[] { 3 }; // Invalid action
// var errors = BarracudaModelParamLoader.CheckModel(model, brainParameters, new SensorComponent[] { sensor_21_20_3, sensor_20_22_3 }, new ActuatorComponent[0]);
// Assert.Greater(errors.Count(), 0);
// brainParameters = GetContinuous2vis8vec2actionBrainParameters();
// brainParameters.VectorActionSpaceType = SpaceType.Discrete;// Invalid SpaceType
// errors = BarracudaModelParamLoader.CheckModel(model, brainParameters, new SensorComponent[] { sensor_21_20_3, sensor_20_22_3 }, new ActuatorComponent[0]);
// Assert.Greater(errors.Count(), 0);
// }
[Test]
public void TestCheckModelThrowsNoModel()

2
com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action_deprecated.nn.meta


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2
com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_recurr_deprecated.nn.meta


fileFormatVersion: 2
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ScriptedImporter:
fileIDToRecycleName:
11400000: main obj

22
ml-agents-envs/mlagents_envs/communicator_objects/agent_action_pb2.py


name='mlagents_envs/communicator_objects/agent_action.proto',
package='communicator_objects',
syntax='proto3',
serialized_pb=_b('\n5mlagents_envs/communicator_objects/agent_action.proto\x12\x14\x63ommunicator_objects\"K\n\x10\x41gentActionProto\x12\x16\n\x0evector_actions\x18\x01 \x03(\x02\x12\r\n\x05value\x18\x04 \x01(\x02J\x04\x08\x02\x10\x03J\x04\x08\x03\x10\x04J\x04\x08\x05\x10\x06\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
serialized_pb=_b('\n5mlagents_envs/communicator_objects/agent_action.proto\x12\x14\x63ommunicator_objects\"\x8c\x01\n\x10\x41gentActionProto\x12!\n\x19vector_actions_deprecated\x18\x01 \x03(\x02\x12\r\n\x05value\x18\x04 \x01(\x02\x12\x1a\n\x12\x63ontinuous_actions\x18\x06 \x03(\x02\x12\x18\n\x10\x64iscrete_actions\x18\x07 \x03(\x05J\x04\x08\x02\x10\x03J\x04\x08\x03\x10\x04J\x04\x08\x05\x10\x06\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
)

containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='vector_actions', full_name='communicator_objects.AgentActionProto.vector_actions', index=0,
name='vector_actions_deprecated', full_name='communicator_objects.AgentActionProto.vector_actions_deprecated', index=0,
number=1, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,

message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='continuous_actions', full_name='communicator_objects.AgentActionProto.continuous_actions', index=2,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='discrete_actions', full_name='communicator_objects.AgentActionProto.discrete_actions', index=3,
number=7, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],

extension_ranges=[],
oneofs=[
],
serialized_start=79,
serialized_end=154,
serialized_start=80,
serialized_end=220,
)
DESCRIPTOR.message_types_by_name['AgentActionProto'] = _AGENTACTIONPROTO

12
ml-agents-envs/mlagents_envs/communicator_objects/agent_action_pb2.pyi


class AgentActionProto(google___protobuf___message___Message):
DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
vector_actions = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___float]
vector_actions_deprecated = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___float]
continuous_actions = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___float]
discrete_actions = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___int]
vector_actions : typing___Optional[typing___Iterable[builtin___float]] = None,
vector_actions_deprecated : typing___Optional[typing___Iterable[builtin___float]] = None,
continuous_actions : typing___Optional[typing___Iterable[builtin___float]] = None,
discrete_actions : typing___Optional[typing___Iterable[builtin___int]] = None,
) -> None: ...
@classmethod
def FromString(cls, s: builtin___bytes) -> AgentActionProto: ...

def ClearField(self, field_name: typing_extensions___Literal[u"value",u"vector_actions"]) -> None: ...
def ClearField(self, field_name: typing_extensions___Literal[u"continuous_actions",u"discrete_actions",u"value",u"vector_actions_deprecated"]) -> None: ...
def ClearField(self, field_name: typing_extensions___Literal[u"value",b"value",u"vector_actions",b"vector_actions"]) -> None: ...
def ClearField(self, field_name: typing_extensions___Literal[u"continuous_actions",b"continuous_actions",u"discrete_actions",b"discrete_actions",u"value",b"value",u"vector_actions_deprecated",b"vector_actions_deprecated"]) -> None: ...

82
ml-agents-envs/mlagents_envs/communicator_objects/brain_parameters_pb2.py


name='mlagents_envs/communicator_objects/brain_parameters.proto',
package='communicator_objects',
syntax='proto3',
serialized_pb=_b('\n9mlagents_envs/communicator_objects/brain_parameters.proto\x12\x14\x63ommunicator_objects\x1a\x33mlagents_envs/communicator_objects/space_type.proto\"\xd9\x01\n\x14\x42rainParametersProto\x12\x1a\n\x12vector_action_size\x18\x03 \x03(\x05\x12\"\n\x1avector_action_descriptions\x18\x05 \x03(\t\x12\x46\n\x18vector_action_space_type\x18\x06 \x01(\x0e\x32$.communicator_objects.SpaceTypeProto\x12\x12\n\nbrain_name\x18\x07 \x01(\t\x12\x13\n\x0bis_training\x18\x08 \x01(\x08J\x04\x08\x01\x10\x02J\x04\x08\x02\x10\x03J\x04\x08\x04\x10\x05\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
serialized_pb=_b('\n9mlagents_envs/communicator_objects/brain_parameters.proto\x12\x14\x63ommunicator_objects\x1a\x33mlagents_envs/communicator_objects/space_type.proto\"\x8b\x01\n\x0f\x41\x63tionSpecProto\x12\x1e\n\x16num_continuous_actions\x18\x01 \x01(\x05\x12\x1c\n\x14num_discrete_actions\x18\x02 \x01(\x05\x12\x1d\n\x15\x64iscrete_branch_sizes\x18\x03 \x03(\x05\x12\x1b\n\x13\x61\x63tion_descriptions\x18\x04 \x03(\t\"\xb6\x02\n\x14\x42rainParametersProto\x12%\n\x1dvector_action_size_deprecated\x18\x03 \x03(\x05\x12-\n%vector_action_descriptions_deprecated\x18\x05 \x03(\t\x12Q\n#vector_action_space_type_deprecated\x18\x06 \x01(\x0e\x32$.communicator_objects.SpaceTypeProto\x12\x12\n\nbrain_name\x18\x07 \x01(\t\x12\x13\n\x0bis_training\x18\x08 \x01(\x08\x12:\n\x0b\x61\x63tion_spec\x18\t \x01(\x0b\x32%.communicator_objects.ActionSpecProtoJ\x04\x08\x01\x10\x02J\x04\x08\x02\x10\x03J\x04\x08\x04\x10\x05\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
,
dependencies=[mlagents__envs_dot_communicator__objects_dot_space__type__pb2.DESCRIPTOR,])

_ACTIONSPECPROTO = _descriptor.Descriptor(
name='ActionSpecProto',
full_name='communicator_objects.ActionSpecProto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='num_continuous_actions', full_name='communicator_objects.ActionSpecProto.num_continuous_actions', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='num_discrete_actions', full_name='communicator_objects.ActionSpecProto.num_discrete_actions', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='discrete_branch_sizes', full_name='communicator_objects.ActionSpecProto.discrete_branch_sizes', index=2,
number=3, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='action_descriptions', full_name='communicator_objects.ActionSpecProto.action_descriptions', index=3,
number=4, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=137,
serialized_end=276,
)
_BRAINPARAMETERSPROTO = _descriptor.Descriptor(
name='BrainParametersProto',
full_name='communicator_objects.BrainParametersProto',

fields=[
_descriptor.FieldDescriptor(
name='vector_action_size', full_name='communicator_objects.BrainParametersProto.vector_action_size', index=0,
name='vector_action_size_deprecated', full_name='communicator_objects.BrainParametersProto.vector_action_size_deprecated', index=0,
number=3, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,

name='vector_action_descriptions', full_name='communicator_objects.BrainParametersProto.vector_action_descriptions', index=1,
name='vector_action_descriptions_deprecated', full_name='communicator_objects.BrainParametersProto.vector_action_descriptions_deprecated', index=1,
number=5, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,

name='vector_action_space_type', full_name='communicator_objects.BrainParametersProto.vector_action_space_type', index=2,
name='vector_action_space_type_deprecated', full_name='communicator_objects.BrainParametersProto.vector_action_space_type_deprecated', index=2,
number=6, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,

message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='action_spec', full_name='communicator_objects.BrainParametersProto.action_spec', index=5,
number=9, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],

extension_ranges=[],
oneofs=[
],
serialized_start=137,
serialized_end=354,
serialized_start=279,
serialized_end=589,
_BRAINPARAMETERSPROTO.fields_by_name['vector_action_space_type'].enum_type = mlagents__envs_dot_communicator__objects_dot_space__type__pb2._SPACETYPEPROTO
_BRAINPARAMETERSPROTO.fields_by_name['vector_action_space_type_deprecated'].enum_type = mlagents__envs_dot_communicator__objects_dot_space__type__pb2._SPACETYPEPROTO
_BRAINPARAMETERSPROTO.fields_by_name['action_spec'].message_type = _ACTIONSPECPROTO
DESCRIPTOR.message_types_by_name['ActionSpecProto'] = _ACTIONSPECPROTO
ActionSpecProto = _reflection.GeneratedProtocolMessageType('ActionSpecProto', (_message.Message,), dict(
DESCRIPTOR = _ACTIONSPECPROTO,
__module__ = 'mlagents_envs.communicator_objects.brain_parameters_pb2'
# @@protoc_insertion_point(class_scope:communicator_objects.ActionSpecProto)
))
_sym_db.RegisterMessage(ActionSpecProto)
BrainParametersProto = _reflection.GeneratedProtocolMessageType('BrainParametersProto', (_message.Message,), dict(
DESCRIPTOR = _BRAINPARAMETERSPROTO,

45
ml-agents-envs/mlagents_envs/communicator_objects/brain_parameters_pb2.pyi


builtin___int = int
class ActionSpecProto(google___protobuf___message___Message):
DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
num_continuous_actions = ... # type: builtin___int
num_discrete_actions = ... # type: builtin___int
discrete_branch_sizes = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___int]
action_descriptions = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[typing___Text]
def __init__(self,
*,
num_continuous_actions : typing___Optional[builtin___int] = None,
num_discrete_actions : typing___Optional[builtin___int] = None,
discrete_branch_sizes : typing___Optional[typing___Iterable[builtin___int]] = None,
action_descriptions : typing___Optional[typing___Iterable[typing___Text]] = None,
) -> None: ...
@classmethod
def FromString(cls, s: builtin___bytes) -> ActionSpecProto: ...
def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
if sys.version_info >= (3,):
def ClearField(self, field_name: typing_extensions___Literal[u"action_descriptions",u"discrete_branch_sizes",u"num_continuous_actions",u"num_discrete_actions"]) -> None: ...
else:
def ClearField(self, field_name: typing_extensions___Literal[u"action_descriptions",b"action_descriptions",u"discrete_branch_sizes",b"discrete_branch_sizes",u"num_continuous_actions",b"num_continuous_actions",u"num_discrete_actions",b"num_discrete_actions"]) -> None: ...
vector_action_size = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___int]
vector_action_descriptions = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[typing___Text]
vector_action_space_type = ... # type: mlagents_envs___communicator_objects___space_type_pb2___SpaceTypeProto
vector_action_size_deprecated = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___int]
vector_action_descriptions_deprecated = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[typing___Text]
vector_action_space_type_deprecated = ... # type: mlagents_envs___communicator_objects___space_type_pb2___SpaceTypeProto
@property
def action_spec(self) -> ActionSpecProto: ...
vector_action_size : typing___Optional[typing___Iterable[builtin___int]] = None,
vector_action_descriptions : typing___Optional[typing___Iterable[typing___Text]] = None,
vector_action_space_type : typing___Optional[mlagents_envs___communicator_objects___space_type_pb2___SpaceTypeProto] = None,
vector_action_size_deprecated : typing___Optional[typing___Iterable[builtin___int]] = None,
vector_action_descriptions_deprecated : typing___Optional[typing___Iterable[typing___Text]] = None,
vector_action_space_type_deprecated : typing___Optional[mlagents_envs___communicator_objects___space_type_pb2___SpaceTypeProto] = None,
action_spec : typing___Optional[ActionSpecProto] = None,
) -> None: ...
@classmethod
def FromString(cls, s: builtin___bytes) -> BrainParametersProto: ...

def ClearField(self, field_name: typing_extensions___Literal[u"brain_name",u"is_training",u"vector_action_descriptions",u"vector_action_size",u"vector_action_space_type"]) -> None: ...
def HasField(self, field_name: typing_extensions___Literal[u"action_spec"]) -> builtin___bool: ...
def ClearField(self, field_name: typing_extensions___Literal[u"action_spec",u"brain_name",u"is_training",u"vector_action_descriptions_deprecated",u"vector_action_size_deprecated",u"vector_action_space_type_deprecated"]) -> None: ...
def ClearField(self, field_name: typing_extensions___Literal[u"brain_name",b"brain_name",u"is_training",b"is_training",u"vector_action_descriptions",b"vector_action_descriptions",u"vector_action_size",b"vector_action_size",u"vector_action_space_type",b"vector_action_space_type"]) -> None: ...
def HasField(self, field_name: typing_extensions___Literal[u"action_spec",b"action_spec"]) -> builtin___bool: ...
def ClearField(self, field_name: typing_extensions___Literal[u"action_spec",b"action_spec",u"brain_name",b"brain_name",u"is_training",b"is_training",u"vector_action_descriptions_deprecated",b"vector_action_descriptions_deprecated",u"vector_action_size_deprecated",b"vector_action_size_deprecated",u"vector_action_space_type_deprecated",b"vector_action_space_type_deprecated"]) -> None: ...

13
ml-agents-envs/mlagents_envs/communicator_objects/capabilities_pb2.py


name='mlagents_envs/communicator_objects/capabilities.proto',
package='communicator_objects',
syntax='proto3',
serialized_pb=_b('\n5mlagents_envs/communicator_objects/capabilities.proto\x12\x14\x63ommunicator_objects\"}\n\x18UnityRLCapabilitiesProto\x12\x1a\n\x12\x62\x61seRLCapabilities\x18\x01 \x01(\x08\x12#\n\x1b\x63oncatenatedPngObservations\x18\x02 \x01(\x08\x12 \n\x18\x63ompressedChannelMapping\x18\x03 \x01(\x08\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
serialized_pb=_b('\n5mlagents_envs/communicator_objects/capabilities.proto\x12\x14\x63ommunicator_objects\"\x94\x01\n\x18UnityRLCapabilitiesProto\x12\x1a\n\x12\x62\x61seRLCapabilities\x18\x01 \x01(\x08\x12#\n\x1b\x63oncatenatedPngObservations\x18\x02 \x01(\x08\x12 \n\x18\x63ompressedChannelMapping\x18\x03 \x01(\x08\x12\x15\n\rhybridActions\x18\x04 \x01(\x08\x42%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
)

message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='hybridActions', full_name='communicator_objects.UnityRLCapabilitiesProto.hybridActions', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],

extension_ranges=[],
oneofs=[
],
serialized_start=79,
serialized_end=204,
serialized_start=80,
serialized_end=228,
)
DESCRIPTOR.message_types_by_name['UnityRLCapabilitiesProto'] = _UNITYRLCAPABILITIESPROTO

6
ml-agents-envs/mlagents_envs/communicator_objects/capabilities_pb2.pyi


baseRLCapabilities = ... # type: builtin___bool
concatenatedPngObservations = ... # type: builtin___bool
compressedChannelMapping = ... # type: builtin___bool
hybridActions = ... # type: builtin___bool
def __init__(self,
*,

hybridActions : typing___Optional[builtin___bool] = None,
) -> None: ...
@classmethod
def FromString(cls, s: builtin___bytes) -> UnityRLCapabilitiesProto: ...

def ClearField(self, field_name: typing_extensions___Literal[u"baseRLCapabilities",u"compressedChannelMapping",u"concatenatedPngObservations"]) -> None: ...
def ClearField(self, field_name: typing_extensions___Literal[u"baseRLCapabilities",u"compressedChannelMapping",u"concatenatedPngObservations",u"hybridActions"]) -> None: ...
def ClearField(self, field_name: typing_extensions___Literal[u"baseRLCapabilities",b"baseRLCapabilities",u"compressedChannelMapping",b"compressedChannelMapping",u"concatenatedPngObservations",b"concatenatedPngObservations"]) -> None: ...
def ClearField(self, field_name: typing_extensions___Literal[u"baseRLCapabilities",b"baseRLCapabilities",u"compressedChannelMapping",b"compressedChannelMapping",u"concatenatedPngObservations",b"concatenatedPngObservations",u"hybridActions",b"hybridActions"]) -> None: ...

21
ml-agents-envs/mlagents_envs/environment.py


# * 1.0.0 - initial version
# * 1.1.0 - support concatenated PNGs for compressed observations.
# * 1.2.0 - support compression mapping for stacked compressed observations.
API_VERSION = "1.2.0"
# * 1.3.0 - support hybrid action spaces.
API_VERSION = "1.3.0"
# Default port that the editor listens on. If an environment executable
# isn't specified, this port will be used.

capabilities.baseRLCapabilities = True
capabilities.concatenatedPngObservations = True
capabilities.compressedChannelMapping = True
capabilities.hybridActions = True
return capabilities
@staticmethod

if n_agents == 0:
continue
for i in range(n_agents):
# TODO: This check will be removed when the oroto supports hybrid actions
if vector_action[b].continuous.shape[1] > 0:
_act = vector_action[b].continuous[i]
else:
_act = vector_action[b].discrete[i]
action = AgentActionProto(vector_actions=_act)
action = AgentActionProto()
if vector_action[b].continuous is not None:
action.vector_actions_deprecated.extend(
vector_action[b].continuous[i]
)
action.continuous_actions.extend(vector_action[b].continuous[i])
if vector_action[b].discrete is not None:
action.vector_actions_deprecated.extend(
vector_action[b].discrete[i]
)
action.discrete_actions.extend(vector_action[b].discrete[i])
rl_in.agent_actions[b].value.extend([action])
rl_in.command = STEP
rl_in.side_channel = bytes(

18
ml-agents-envs/mlagents_envs/mock_communicator.py


from .communicator import Communicator
from .environment import UnityEnvironment
from mlagents_envs.communicator_objects.unity_rl_output_pb2 import UnityRLOutputProto
from mlagents_envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
from mlagents_envs.communicator_objects.brain_parameters_pb2 import (
BrainParametersProto,
ActionSpecProto,
)
from mlagents_envs.communicator_objects.unity_rl_initialization_output_pb2 import (
UnityRLInitializationOutputProto,
)

NONE as COMPRESSION_TYPE_NONE,
PNG as COMPRESSION_TYPE_PNG,
)
from mlagents_envs.communicator_objects.space_type_pb2 import discrete, continuous
class MockCommunicator(Communicator):

self.vec_obs_size = vec_obs_size
def initialize(self, inputs: UnityInputProto) -> UnityOutputProto:
if self.is_discrete:
action_spec = ActionSpecProto(
num_discrete_actions=2, discrete_branch_sizes=[3, 2]
)
else:
action_spec = ActionSpecProto(num_continuous_actions=2)
vector_action_size=[2],
vector_action_descriptions=["", ""],
vector_action_space_type=discrete if self.is_discrete else continuous,
brain_name=self.brain_name,
is_training=True,
brain_name=self.brain_name, is_training=True, action_spec=action_spec
)
rl_init = UnityRLInitializationOutputProto(
name="RealFakeAcademy",

21
ml-agents-envs/mlagents_envs/rpc_utils.py


:return: BehaviorSpec object.
"""
observation_shape = [tuple(obs.shape) for obs in agent_info.observations]
if brain_param_proto.vector_action_space_type == 1:
action_spec = ActionSpec(brain_param_proto.vector_action_size[0], ())
# proto from comminicator < v1.3 does not set action spec, use deprecated fields instead
if (
brain_param_proto.action_spec.num_continuous_actions == 0
and brain_param_proto.action_spec.num_discrete_actions == 0
):
if brain_param_proto.vector_action_space_type_deprecated == 1:
action_spec = ActionSpec(
brain_param_proto.vector_action_size_deprecated[0], ()
)
else:
action_spec = ActionSpec(
0, tuple(brain_param_proto.vector_action_size_deprecated)
)
action_spec = ActionSpec(0, tuple(brain_param_proto.vector_action_size))
action_spec_proto = brain_param_proto.action_spec
action_spec = ActionSpec(
action_spec_proto.num_continuous_actions,
tuple(branch for branch in action_spec_proto.discrete_branch_sizes),
)
return BehaviorSpec(observation_shape, action_spec)

33
ml-agents-envs/mlagents_envs/tests/test_rpc_utils.py


return agent_info_protos
# The arguments here are the DecisionSteps, TerminalSteps and actions for a single agent name
# The arguments here are the DecisionSteps, TerminalSteps and continuous/discrete actions for a single agent name
decision_steps: DecisionSteps, terminal_steps: TerminalSteps, actions: np.ndarray
decision_steps: DecisionSteps,
terminal_steps: TerminalSteps,
continuous_actions: np.ndarray,
discrete_actions: np.ndarray,
agent_action_protos = [
AgentActionProto(vector_actions=action) for action in actions
]
agent_action_protos = []
num_agents = (
len(continuous_actions)
if continuous_actions is not None
else len(discrete_actions)
)
for i in range(num_agents):
proto = AgentActionProto()
if continuous_actions is not None:
proto.continuous_actions.extend([continuous_actions[i]])
proto.vector_actions_deprecated.extend([continuous_actions[i]])
if discrete_actions is not None:
proto.discrete_actions.extend([discrete_actions[i]])
proto.vector_actions_deprecated.extend([discrete_actions[i]])
agent_action_protos.append(proto)
agent_info_action_pair_protos = [
AgentInfoActionPairProto(agent_info=agent_info_proto, action_info=action_proto)
for agent_info_proto, action_proto in zip(

def test_agent_behavior_spec_from_proto():
agent_proto = generate_list_agent_proto(1, [(3,), (4,)])[0]
bp = BrainParametersProto()
bp.vector_action_size.extend([5, 4])
bp.vector_action_space_type = 0
bp.vector_action_size_deprecated.extend([5, 4])
bp.vector_action_space_type_deprecated = 0
behavior_spec = behavior_spec_from_proto(bp, agent_proto)
assert behavior_spec.action_spec.is_discrete()
assert not behavior_spec.action_spec.is_continuous()

bp = BrainParametersProto()
bp.vector_action_size.extend([6])
bp.vector_action_space_type = 1
bp.vector_action_size_deprecated.extend([6])
bp.vector_action_space_type_deprecated = 1
behavior_spec = behavior_spec_from_proto(bp, agent_proto)
assert not behavior_spec.action_spec.is_discrete()
assert behavior_spec.action_spec.is_continuous()

14
ml-agents/mlagents/trainers/demo_loader.py


[next_pair_info.agent_info], behavior_spec
)
previous_action = (
np.array(pair_infos[idx].action_info.vector_actions, dtype=np.float32) * 0
np.array(
pair_infos[idx].action_info.vector_actions_deprecated, dtype=np.float32
)
* 0
pair_infos[idx - 1].action_info.vector_actions, dtype=np.float32
pair_infos[idx - 1].action_info.vector_actions_deprecated,
dtype=np.float32,
)
next_done = len(next_terminal_step) == 1

for i, obs in enumerate(split_obs.visual_observations):
demo_raw_buffer["visual_obs%d" % i].append(obs)
demo_raw_buffer["vector_obs"].append(split_obs.vector_observations)
# TODO: update to read from the new proto format
# TODO: update the demonstraction files and read from the new proto format
current_pair_info.action_info.vector_actions
current_pair_info.action_info.vector_actions_deprecated
current_pair_info.action_info.vector_actions
current_pair_info.action_info.vector_actions_deprecated
)
demo_raw_buffer["prev_action"].append(previous_action)
if next_done:

19
ml-agents/mlagents/trainers/tests/simple_test_envs.py


def step(self) -> None:
super().step()
for name in self.names:
if self.action_spec.discrete_size > 0:
action = self.action[name].discrete
else:
action = self.action[name].continuous
discrete_actions = (
self.action[name].discrete
if self.action_spec.discrete_size > 0
else None
)
continuous_actions = (
self.action[name].continuous
if self.action_spec.continuous_size > 0
else None
)
self.step_result[name][0], self.step_result[name][1], action
self.step_result[name][0],
self.step_result[name][1],
continuous_actions,
discrete_actions,
)
self.demonstration_protos[name] = self.demonstration_protos[name][
-self.n_demos :

8
ml-agents/mlagents/trainers/tests/tensorflow/test_simple_rl.py


agent_info_protos = env.demonstration_protos[BRAIN_NAME]
meta_data_proto = DemonstrationMetaProto()
brain_param_proto = BrainParametersProto(
vector_action_size=[2] if use_discrete else [1],
vector_action_descriptions=[""],
vector_action_space_type=discrete if use_discrete else continuous,
vector_action_size_deprecated=[2] if use_discrete else [1],
vector_action_descriptions_deprecated=[""],
vector_action_space_type_deprecated=discrete
if use_discrete
else continuous,
brain_name=BRAIN_NAME,
is_training=True,
)

18
ml-agents/mlagents/trainers/tests/torch/test_simple_rl.py


from mlagents_envs.communicator_objects.demonstration_meta_pb2 import (
DemonstrationMetaProto,
)
from mlagents_envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
from mlagents_envs.communicator_objects.space_type_pb2 import discrete, continuous
from mlagents_envs.communicator_objects.brain_parameters_pb2 import (
BrainParametersProto,
ActionSpecProto,
)
from mlagents.trainers.tests.dummy_config import ppo_dummy_config, sac_dummy_config
from mlagents.trainers.tests.check_env_trains import (

env.solve()
agent_info_protos = env.demonstration_protos[BRAIN_NAME]
meta_data_proto = DemonstrationMetaProto()
continuous_action_size, discrete_action_size = action_sizes
action_spec_proto = ActionSpecProto(
num_continuous_actions=continuous_action_size,
num_discrete_actions=discrete_action_size,
discrete_branch_sizes=[2] if discrete_action_size > 0 else None,
)
vector_action_size=[2] if action_sizes else [1],
vector_action_descriptions=[""],
vector_action_space_type=discrete if action_sizes else continuous,
brain_name=BRAIN_NAME,
is_training=True,
brain_name=BRAIN_NAME, is_training=True, action_spec=action_spec_proto
)
action_type = "Discrete" if action_sizes else "Continuous"
demo_path_name = "1DTest" + action_type + ".demo"

28
ml-agents/mlagents/trainers/torch/action_model.py


:return: A tuple of torch tensors corresponding to the inference output
"""
dists = self._get_dists(inputs, masks)
out_list: List[torch.Tensor] = []
# This checks None because mypy complains otherwise
if dists.continuous is not None:
continuous_action_export = dists.continuous.exported_model_output()
if self._clip_action_on_export:
continuous_action_export = (
torch.clamp(continuous_action_export, -3, 3) / 3
)
out_list.append(continuous_action_export)
if dists.discrete is not None:
for discrete_dist in dists.discrete:
out_list.append(discrete_dist.exported_model_output())
return torch.cat(out_list, dim=1)
continuous_out, discrete_out, action_out_deprecated = None, None, None
if self.action_spec.continuous_size > 0 and dists.continuous is not None:
continuous_out = dists.continuous.exported_model_output()
action_out_deprecated = dists.continuous.exported_model_output()
if self.action_spec.discrete_size > 0 and dists.discrete is not None:
discrete_out_list = [
discrete_dist.exported_model_output()
for discrete_dist in dists.discrete
]
discrete_out = torch.cat(discrete_out_list, dim=1)
action_out_deprecated = torch.cat(discrete_out_list, dim=1)
# deprecated action field does not support hybrid action
if self.action_spec.continuous_size > 0 and self.action_spec.discrete_size > 0:
action_out_deprecated = None
return continuous_out, discrete_out, action_out_deprecated
def forward(
self, inputs: torch.Tensor, masks: torch.Tensor

31
ml-agents/mlagents/trainers/torch/model_serialization.py


+ [f"visual_observation_{i}" for i in range(self.policy.vis_obs_size)]
+ ["action_masks", "memories"]
)
self.dynamic_axes = {name: {0: "batch"} for name in self.input_names}
self.output_names = [
"action",
"version_number",
"memory_size",
"is_continuous_control",
"action_output_shape",
]
self.dynamic_axes = {name: {0: "batch"} for name in self.input_names}
self.dynamic_axes.update({"action": {0: "batch"}})
self.output_names = ["version_number", "memory_size"]
if self.policy.behavior_spec.action_spec.continuous_size > 0:
self.output_names += [
"continuous_actions",
"continuous_action_output_shape",
]
self.dynamic_axes.update({"continuous_actions": {0: "batch"}})
if self.policy.behavior_spec.action_spec.discrete_size > 0:
self.output_names += ["discrete_actions", "discrete_action_output_shape"]
self.dynamic_axes.update({"discrete_actions": {0: "batch"}})
if (
self.policy.behavior_spec.action_spec.continuous_size == 0
or self.policy.behavior_spec.action_spec.discrete_size == 0
):
self.output_names += [
"action",
"is_continuous_control",
"action_output_shape",
]
self.dynamic_axes.update({"action": {0: "batch"}})
def export_policy_model(self, output_filepath: str) -> None:
"""

43
ml-agents/mlagents/trainers/torch/networks.py


from typing import Callable, List, Dict, Tuple, Optional
from typing import Callable, List, Dict, Tuple, Optional, Union
import abc
from mlagents.torch_utils import torch, nn

vis_inputs: List[torch.Tensor],
masks: Optional[torch.Tensor] = None,
memories: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, int, int, int, int]:
) -> Tuple[Union[int, torch.Tensor], ...]:
"""
Forward pass of the Actor for inference. This is required for export to ONNX, and
the inputs and outputs of this method should not be changed without a respective change

super().__init__()
self.action_spec = action_spec
self.version_number = torch.nn.Parameter(torch.Tensor([2.0]))
self.is_continuous_int = torch.nn.Parameter(
self.is_continuous_int_deprecated = torch.nn.Parameter(
self.act_size_vector = torch.nn.Parameter(
self.continuous_act_size_vector = torch.nn.Parameter(
torch.Tensor([int(self.action_spec.continuous_size)]), requires_grad=False
)
# TODO: export list of branch sizes instead of sum
self.discrete_act_size_vector = torch.nn.Parameter(
torch.Tensor([sum(self.action_spec.discrete_branches)]), requires_grad=False
)
self.act_size_vector_deprecated = torch.nn.Parameter(
torch.Tensor(
[
self.action_spec.continuous_size

vis_inputs: List[torch.Tensor],
masks: Optional[torch.Tensor] = None,
memories: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, int, int, int, int]:
) -> Tuple[Union[int, torch.Tensor], ...]:
At this moment, torch.onnx.export() doesn't accept None as tensor to be exported,
so the size of return tuple varies with action spec.
action_out = self.action_model.get_action_out(encoding, masks)
return (
action_out,
cont_action_out, disc_action_out, action_out_deprecated = self.action_model.get_action_out(
encoding, masks
)
export_out = [
self.is_continuous_int,
self.act_size_vector,
)
]
if self.action_spec.continuous_size > 0:
export_out += [cont_action_out, self.continuous_act_size_vector]
if self.action_spec.discrete_size > 0:
export_out += [disc_action_out, self.discrete_act_size_vector]
# Only export deprecated nodes with non-hybrid action spec
if self.action_spec.continuous_size == 0 or self.action_spec.discrete_size == 0:
export_out += [
action_out_deprecated,
self.is_continuous_int_deprecated,
self.act_size_vector_deprecated,
]
return tuple(export_out)
class SharedActorCritic(SimpleActor, ActorCritic):

4
protobuf-definitions/proto/mlagents_envs/communicator_objects/agent_action.proto


package communicator_objects;
message AgentActionProto {
repeated float vector_actions = 1;
repeated float vector_actions_deprecated = 1; // mark as deprecated in communicator v1.3.0
repeated float continuous_actions = 6;
repeated int32 discrete_actions = 7;
}

14
protobuf-definitions/proto/mlagents_envs/communicator_objects/brain_parameters.proto


option csharp_namespace = "Unity.MLAgents.CommunicatorObjects";
package communicator_objects;
message ActionSpecProto {
int32 num_continuous_actions = 1;
int32 num_discrete_actions = 2;
repeated int32 discrete_branch_sizes = 3;
repeated string action_descriptions = 4;
}
repeated int32 vector_action_size = 3;
repeated int32 vector_action_size_deprecated = 3; // mark as deprecated in communicator v1.3.0
repeated string vector_action_descriptions = 5;
SpaceTypeProto vector_action_space_type = 6;
repeated string vector_action_descriptions_deprecated = 5; // mark as deprecated in communicator v1.3.0
SpaceTypeProto vector_action_space_type_deprecated = 6; // mark as deprecated in communicator v1.3.0
ActionSpecProto action_spec = 9;
}

3
protobuf-definitions/proto/mlagents_envs/communicator_objects/capabilities.proto


// compression mapping for stacking compressed observations.
bool compressedChannelMapping = 3;
// support for hybrid action spaces (discrete + continuous)
bool hybridActions = 4;
}

360
com.unity.ml-agents/Runtime/Inference/BarracudaModelExtensions.cs


using System.Collections.Generic;
using System.Linq;
using Unity.Barracuda;
namespace Unity.MLAgents.Inference
{
/// <summary>
/// Barracuda Model extension methods.
/// </summary>
internal static class BarracudaModelExtensions
{
/// <summary>
/// Get array of the input tensor names of the model.
/// </summary>
/// <param name="model">
/// The Barracuda engine model for loading static parameters.
/// </param>
/// <returns>Array of the input tensor names of the model</returns>
public static string[] GetInputNames(this Model model)
{
var names = new List<string>();
if (model == null)
return names.ToArray();
foreach (var input in model.inputs)
{
names.Add(input.name);
}
foreach (var mem in model.memories)
{
names.Add(mem.input);
}
names.Sort();
return names.ToArray();
}
/// <summary>
/// Generates the Tensor inputs that are expected to be present in the Model.
/// </summary>
/// <param name="model">
/// The Barracuda engine model for loading static parameters.
/// </param>
/// <returns>TensorProxy IEnumerable with the expected Tensor inputs.</returns>
public static IReadOnlyList<TensorProxy> GetInputTensors(this Model model)
{
var tensors = new List<TensorProxy>();
if (model == null)
return tensors;
foreach (var input in model.inputs)
{
tensors.Add(new TensorProxy
{
name = input.name,
valueType = TensorProxy.TensorType.FloatingPoint,
data = null,
shape = input.shape.Select(i => (long)i).ToArray()
});
}
foreach (var mem in model.memories)
{
tensors.Add(new TensorProxy
{
name = mem.input,
valueType = TensorProxy.TensorType.FloatingPoint,
data = null,
shape = TensorUtils.TensorShapeFromBarracuda(mem.shape)
});
}
tensors.Sort((el1, el2) => el1.name.CompareTo(el2.name));
return tensors;
}
/// <summary>
/// Get number of visual observation inputs to the model.
/// </summary>
/// <param name="model">
/// The Barracuda engine model for loading static parameters.
/// </param>
/// <returns>Number of visual observation inputs to the model</returns>
public static int GetNumVisualInputs(this Model model)
{
var count = 0;
if (model == null)
return count;
foreach (var input in model.inputs)
{
if (input.name.StartsWith(TensorNames.VisualObservationPlaceholderPrefix))
{
count++;
}
}
return count;
}
/// <summary>
/// Get array of the output tensor names of the model.
/// </summary>
/// <param name="model">
/// The Barracuda engine model for loading static parameters.
/// </param>
/// <returns>Array of the output tensor names of the model</returns>
public static string[] GetOutputNames(this Model model)
{
var names = new List<string>();
if (model == null)
{
return names.ToArray();
}
if (model.HasContinuousOutputs())
{
names.Add(model.ContinuousOutputName());
}
if (model.HasDiscreteOutputs())
{
names.Add(model.DiscreteOutputName());
}
var memory = (int)model.GetTensorByName(TensorNames.MemorySize)[0];
if (memory > 0)
{
foreach (var mem in model.memories)
{
names.Add(mem.output);
}
}
names.Sort();
return names.ToArray();
}
/// <summary>
/// Check if the model has continuous action outputs.
/// </summary>
/// <param name="model">
/// The Barracuda engine model for loading static parameters.
/// </param>
/// <returns>True if the model has continuous action outputs.</returns>
public static bool HasContinuousOutputs(this Model model)
{
if (model == null)
return false;
if (!model.HasHybridSupport())
{
return (int)model.GetTensorByName(TensorNames.IsContinuousControlDeprecated)[0] > 0;
}
else
{
return model.outputs.Contains(TensorNames.ContinuousActionOutput) &&
(int)model.GetTensorByName(TensorNames.ContinuousActionOutputShape)[0] > 0;
}
}
/// <summary>
/// Continuous action output size of the model.
/// </summary>
/// <param name="model">
/// The Barracuda engine model for loading static parameters.
/// </param>
/// <returns>Size of continuous action output.</returns>
public static int ContinuousOutputSize(this Model model)
{
if (model == null)
return 0;
if (!model.HasHybridSupport())
{
return (int)model.GetTensorByName(TensorNames.IsContinuousControlDeprecated)[0] > 0 ?
(int)model.GetTensorByName(TensorNames.ActionOutputShapeDeprecated)[0] : 0;
}
else
{
var continuousOutputShape = model.GetTensorByName(TensorNames.ContinuousActionOutputShape);
return continuousOutputShape == null ? 0 : (int)continuousOutputShape[0];
}
}
/// <summary>
/// Continuous action output tensor name of the model.
/// </summary>
/// <param name="model">
/// The Barracuda engine model for loading static parameters.
/// </param>
/// <returns>Tensor name of continuous action output.</returns>
public static string ContinuousOutputName(this Model model)
{
if (model == null)
return null;
if (!model.HasHybridSupport())
{
return TensorNames.ActionOutputDeprecated;
}
else
{
return TensorNames.ContinuousActionOutput;
}
}
/// <summary>
/// Check if the model has discrete action outputs.
/// </summary>
/// <param name="model">
/// The Barracuda engine model for loading static parameters.
/// </param>
/// <returns>True if the model has discrete action outputs.</returns>
public static bool HasDiscreteOutputs(this Model model)
{
if (model == null)
return false;
if (!model.HasHybridSupport())
{
return (int)model.GetTensorByName(TensorNames.IsContinuousControlDeprecated)[0] == 0;
}
else
{
return model.outputs.Contains(TensorNames.DiscreteActionOutput) &&
(int)model.GetTensorByName(TensorNames.DiscreteActionOutputShape)[0] > 0;
}
}
/// <summary>
/// Discrete action output size of the model. This is equal to the sum of the branch sizes.
/// </summary>
/// <param name="model">
/// The Barracuda engine model for loading static parameters.
/// </param>
/// <returns>Size of discrete action output.</returns>
public static int DiscreteOutputSize(this Model model)
{
if (model == null)
return 0;
if (!model.HasHybridSupport())
{
return (int)model.GetTensorByName(TensorNames.IsContinuousControlDeprecated)[0] > 0 ?
0 : (int)model.GetTensorByName(TensorNames.ActionOutputShapeDeprecated)[0];
}
else
{
var discreteOutputShape = model.GetTensorByName(TensorNames.DiscreteActionOutputShape);
return discreteOutputShape == null ? 0 : (int)discreteOutputShape[0];
}
}
/// <summary>
/// Discrete action output tensor name of the model.
/// </summary>
/// <param name="model">
/// The Barracuda engine model for loading static parameters.
/// </param>
/// <returns>Tensor name of discrete action output.</returns>
public static string DiscreteOutputName(this Model model)
{
if (model == null)
return null;
if (!model.HasHybridSupport())
{
return TensorNames.ActionOutputDeprecated;
}
else
{
return TensorNames.DiscreteActionOutput;
}
}
/// <summary>
/// Check if the model supports hybrid action spaces.
/// If not, the model should be handled differently and use the deprecated fields.
/// </summary>
/// <param name="model">
/// The Barracuda engine model for loading static parameters.
/// </param>
/// <returns>True if the model supports hybrid action spaces.</returns>
public static bool HasHybridSupport(this Model model)
{
return model == null ||
model.outputs.Contains(TensorNames.ContinuousActionOutput) ||
model.outputs.Contains(TensorNames.DiscreteActionOutput);
}
/// <summary>
/// Check if the model contains all the expected input/output tensors.
/// </summary>
/// <param name="model">
/// The Barracuda engine model for loading static parameters.
/// </param>
/// <returns>True if the model contains all the expected tensors.</returns>
public static bool CheckExpectedTensors(this Model model, List<string> failedModelChecks)
{
// Check the presence of model version
var modelApiVersionTensor = model.GetTensorByName(TensorNames.VersionNumber);
if (modelApiVersionTensor == null)
{
failedModelChecks.Add($"Required constant \"{TensorNames.VersionNumber}\" was not found in the model file.");
return false;
}
// Check the presence of memory size
var memorySizeTensor = model.GetTensorByName(TensorNames.MemorySize);
if (memorySizeTensor == null)
{
failedModelChecks.Add($"Required constant \"{TensorNames.MemorySize}\" was not found in the model file.");
return false;
}
// Check the presence of action output tensor
if (!model.outputs.Contains(TensorNames.ActionOutputDeprecated) &&
!model.outputs.Contains(TensorNames.ContinuousActionOutput) &&
!model.outputs.Contains(TensorNames.DiscreteActionOutput))
{
failedModelChecks.Add("The model does not contain any Action Output Node.");
return false;
}
// Check the presence of action output shape tensor
if (!model.HasHybridSupport())
{
if (model.GetTensorByName(TensorNames.ActionOutputShapeDeprecated) == null)
{
failedModelChecks.Add("The model does not contain any Action Output Shape Node.");
return false;
}
if (model.GetTensorByName(TensorNames.IsContinuousControlDeprecated) == null)
{
failedModelChecks.Add($"Required constant \"{TensorNames.IsContinuousControlDeprecated}\" was not found in the model file. " +
"This is only required for model that uses a deprecated model format.");
return false;
}
}
else
{
if (model.outputs.Contains(TensorNames.ContinuousActionOutput) &&
model.GetTensorByName(TensorNames.ContinuousActionOutputShape) == null)
{
failedModelChecks.Add("The model uses continuous action but does not contain Continuous Action Output Shape Node.");
return false;
}
if (model.outputs.Contains(TensorNames.DiscreteActionOutput) &&
model.GetTensorByName(TensorNames.DiscreteActionOutputShape) == null)
{
failedModelChecks.Add("The model uses discrete action but does not contain Discrete Action Output Shape Node.");
return false;
}
}
return true;
}
}
}

11
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com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action.onnx
文件差异内容过多而无法显示
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14
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14
com.unity.ml-agents/Tests/Editor/TestModels/hybrid0vis53vec_3c_2daction.onnx.meta


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/com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action.nn → /com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action_deprecated.nn

/com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_recurr.nn → /com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_recurr_deprecated.nn

/com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action.nn.meta → /com.unity.ml-agents/Tests/Editor/TestModels/continuous2vis8vec2action_deprecated.nn.meta

/com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_recurr.nn.meta → /com.unity.ml-agents/Tests/Editor/TestModels/discrete1vis0vec_2_3action_recurr_deprecated.nn.meta

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