Merge branch 'develop-centralizedcritic-mm' into develop-zombieteammanager

Project/Assets/ML-Agents/Examples/Hallway/Scripts/HallwayCollabAgent.cs

 
     [HideInInspector]
     public int selection = 0;
+
+    public override void Initialize()
+    {
+        base.Initialize();
+        if (isSpotter)
+        {
+            var teamManager = new HallwayTeamManager();
+            SetTeamManager(teamManager);
+            teammate.SetTeamManager(teamManager);
+        }
+    }
     public override void OnEpisodeBegin()
     {
         m_Message = -1;

Project/Assets/ML-Agents/Examples/Hallway/TFModels/HallwayCollab.onnx.meta

   fileIDToRecycleName:
     11400000: main obj
     11400002: model data
+    2186277476908879412: ImportLogs
   externalObjects: {}
   userData: 
   assetBundleName: 

com.unity.ml-agents/Editor/BehaviorParametersEditor.cs

         const string k_InferenceDeviceName = "m_InferenceDevice";
         const string k_BehaviorTypeName = "m_BehaviorType";
         const string k_TeamIdName = "TeamId";
-        const string k_GroupIdName = "GroupId";
         const string k_UseChildSensorsName = "m_UseChildSensors";
         const string k_ObservableAttributeHandlingName = "m_ObservableAttributeHandling";
 
             }
             needPolicyUpdate = needPolicyUpdate || EditorGUI.EndChangeCheck();
 
-            EditorGUILayout.PropertyField(so.FindProperty(k_GroupIdName));
             EditorGUILayout.PropertyField(so.FindProperty(k_TeamIdName));
             EditorGUI.BeginDisabledGroup(!EditorUtilities.CanUpdateModelProperties());
             {

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

         /// </summary>
         public int episodeId;
 
+        /// <summary>
+        /// Team Manager identifier.
+        /// </summary>
+        public string teamManagerId;
+
         public void ClearActions()
         {
             storedActions.Clear();
         /// </summary>
         float[] m_LegacyActionCache;
 
+        private ITeamManager m_TeamManager;
+
         /// <summary>
         /// Called when the attached [GameObject] becomes enabled and active.
         /// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
                 new int[m_ActuatorManager.NumDiscreteActions]
             );
 
+            if (m_TeamManager != null)
+            {
+                m_Info.teamManagerId = m_TeamManager.GetId();
+            }
+
             // The first time the Academy resets, all Agents in the scene will be
             // forced to reset through the <see cref="AgentForceReset"/> event.
             // To avoid the Agent resetting twice, the Agents will not begin their
         /// <summary>
         /// The reason that the Agent has been set to "done".
         /// </summary>
-        enum DoneReason
+        public enum DoneReason
         {
             /// <summary>
             /// The episode was ended manually by calling <see cref="EndEpisode"/>.
                 }
             }
             // Request the last decision with no callbacks
-            // We request a decision so Python knows the Agent is done immediately
-            m_Brain?.RequestDecision(m_Info, sensors);
-            ResetSensors();
+            if (m_TeamManager != null)
+            {
+                // Send final observations to TeamManager if it exists.
+                // The TeamManager is responsible to keeping track of the Agent after it's
+                // done, including propagating any "posthumous" rewards.
+                m_TeamManager.OnAgentDone(this, doneReason, sensors);
+            }
+            else
+            {
+                SendDoneToTrainer();
+            }
 
             // We also have to write any to any DemonstationStores so that they get the "done" flag.
             foreach (var demoWriter in DemonstrationWriters)
             m_RequestAction = false;
             m_RequestDecision = false;
             m_Info.storedActions.Clear();
+        }
+
+        public void SendDoneToTrainer()
+        {
+            // We request a decision so Python knows the Agent is done immediately
+            m_Brain?.RequestDecision(m_Info, sensors);
+            ResetSensors();
         }
 
         /// <summary>
             var actions = m_Brain?.DecideAction() ?? new ActionBuffers();
             m_Info.CopyActions(actions);
             m_ActuatorManager.UpdateActions(actions);
+        }
+
+        public void SetTeamManager(ITeamManager teamManager)
+        {
+            m_TeamManager = teamManager;
+            m_Info.teamManagerId = teamManager?.GetId();
+            teamManager?.RegisterAgent(this);
         }
     }
 }

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

                 agentInfoProto.ActionMask.AddRange(ai.discreteActionMasks);
             }
 
+            if (ai.teamManagerId != null)
+            {
+                agentInfoProto.TeamManagerId = ai.teamManagerId;
+            }
+
             return agentInfoProto;
         }
 

com.unity.ml-agents/Runtime/Grpc/CommunicatorObjects/AgentInfo.cs

           string.Concat(
             "CjNtbGFnZW50c19lbnZzL2NvbW11bmljYXRvcl9vYmplY3RzL2FnZW50X2lu",
             "Zm8ucHJvdG8SFGNvbW11bmljYXRvcl9vYmplY3RzGjRtbGFnZW50c19lbnZz",
-            "L2NvbW11bmljYXRvcl9vYmplY3RzL29ic2VydmF0aW9uLnByb3RvItEBCg5B",
+            "L2NvbW11bmljYXRvcl9vYmplY3RzL29ic2VydmF0aW9uLnByb3RvIuoBCg5B",
-            "YXRvcl9vYmplY3RzLk9ic2VydmF0aW9uUHJvdG9KBAgBEAJKBAgCEANKBAgD",
-            "EARKBAgEEAVKBAgFEAZKBAgGEAdKBAgMEA1CJaoCIlVuaXR5Lk1MQWdlbnRz",
-            "LkNvbW11bmljYXRvck9iamVjdHNiBnByb3RvMw=="));
+            "YXRvcl9vYmplY3RzLk9ic2VydmF0aW9uUHJvdG8SFwoPdGVhbV9tYW5hZ2Vy",
+            "X2lkGA4gASgJSgQIARACSgQIAhADSgQIAxAESgQIBBAFSgQIBRAGSgQIBhAH",
+            "SgQIDBANQiWqAiJVbml0eS5NTEFnZW50cy5Db21tdW5pY2F0b3JPYmplY3Rz",
+            "YgZwcm90bzM="));
-            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.AgentInfoProto), global::Unity.MLAgents.CommunicatorObjects.AgentInfoProto.Parser, new[]{ "Reward", "Done", "MaxStepReached", "Id", "ActionMask", "Observations" }, null, null, null)
+            new pbr::GeneratedClrTypeInfo(typeof(global::Unity.MLAgents.CommunicatorObjects.AgentInfoProto), global::Unity.MLAgents.CommunicatorObjects.AgentInfoProto.Parser, new[]{ "Reward", "Done", "MaxStepReached", "Id", "ActionMask", "Observations", "TeamManagerId" }, null, null, null)
           }));
     }
     #endregion
       id_ = other.id_;
       actionMask_ = other.actionMask_.Clone();
       observations_ = other.observations_.Clone();
+      teamManagerId_ = other.teamManagerId_;
       _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);
     }
 
       get { return observations_; }
     }
 
+    /// <summary>Field number for the "team_manager_id" field.</summary>
+    public const int TeamManagerIdFieldNumber = 14;
+    private string teamManagerId_ = "";
+    [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
+    public string TeamManagerId {
+      get { return teamManagerId_; }
+      set {
+        teamManagerId_ = pb::ProtoPreconditions.CheckNotNull(value, "value");
+      }
+    }
+
     [global::System.Diagnostics.DebuggerNonUserCodeAttribute]
     public override bool Equals(object other) {
       return Equals(other as AgentInfoProto);
       if (Id != other.Id) return false;
       if(!actionMask_.Equals(other.actionMask_)) return false;
       if(!observations_.Equals(other.observations_)) return false;
+      if (TeamManagerId != other.TeamManagerId) return false;
       return Equals(_unknownFields, other._unknownFields);
     }
 
       if (Id != 0) hash ^= Id.GetHashCode();
       hash ^= actionMask_.GetHashCode();
       hash ^= observations_.GetHashCode();
+      if (TeamManagerId.Length != 0) hash ^= TeamManagerId.GetHashCode();
       if (_unknownFields != null) {
         hash ^= _unknownFields.GetHashCode();
       }
       }
       actionMask_.WriteTo(output, _repeated_actionMask_codec);
       observations_.WriteTo(output, _repeated_observations_codec);
+      if (TeamManagerId.Length != 0) {
+        output.WriteRawTag(114);
+        output.WriteString(TeamManagerId);
+      }
       if (_unknownFields != null) {
         _unknownFields.WriteTo(output);
       }
       }
       size += actionMask_.CalculateSize(_repeated_actionMask_codec);
       size += observations_.CalculateSize(_repeated_observations_codec);
+      if (TeamManagerId.Length != 0) {
+        size += 1 + pb::CodedOutputStream.ComputeStringSize(TeamManagerId);
+      }
       if (_unknownFields != null) {
         size += _unknownFields.CalculateSize();
       }
       }
       actionMask_.Add(other.actionMask_);
       observations_.Add(other.observations_);
+      if (other.TeamManagerId.Length != 0) {
+        TeamManagerId = other.TeamManagerId;
+      }
       _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);
     }
 
           }
           case 106: {
             observations_.AddEntriesFrom(input, _repeated_observations_codec);
+            break;
+          }
+          case 114: {
+            TeamManagerId = input.ReadString();
             break;
           }
         }

com.unity.ml-agents/Runtime/Policies/BehaviorParameters.cs

         [HideInInspector, SerializeField, FormerlySerializedAs("m_TeamID")]
         public int TeamId;
 
-        /// <summary>
-        /// The group ID for this behavior.
-        /// </summary>
-        [HideInInspector, SerializeField]
-        [Tooltip("Assign the same Group ID to all Agents in the same Area.")]
-        public int GroupId;
         // TODO properties here instead of Agent
 
         [FormerlySerializedAs("m_useChildSensors")]
         /// </summary>
         public string FullyQualifiedBehaviorName
         {
-            get { return m_BehaviorName + "?team=" + TeamId + "&group=" + GroupId; }
+            get { return m_BehaviorName + "?team=" + TeamId; }
         }
 
         internal IPolicy GeneratePolicy(ActionSpec actionSpec, HeuristicPolicy.ActionGenerator heuristic)

ml-agents-envs/mlagents_envs/base_env.py

     reward: float
     agent_id: AgentId
     action_mask: Optional[List[np.ndarray]]
+    team_manager_id: Optional[str]
 
 
 class DecisionSteps(Mapping):
      this simulation step.
     """
 
-    def __init__(self, obs, reward, agent_id, action_mask):
+    def __init__(self, obs, reward, agent_id, action_mask, team_manager_id=None):
+        self.team_manager_id: Optional[List[str]] = team_manager_id
         self.action_mask: Optional[List[np.ndarray]] = action_mask
         self._agent_id_to_index: Optional[Dict[AgentId, int]] = None
 
             agent_mask = []
             for mask in self.action_mask:
                 agent_mask.append(mask[agent_index])
+        team_manager_id = None
+        if self.team_manager_id is not None and self.team_manager_id != "":
+            team_manager_id = self.team_manager_id[agent_index]
+            team_manager_id=team_manager_id,
         )
 
     def __iter__(self) -> Iterator[Any]:
             reward=np.zeros(0, dtype=np.float32),
             agent_id=np.zeros(0, dtype=np.int32),
             action_mask=None,
+            team_manager_id=None,
         )
 
 
     reward: float
     interrupted: bool
     agent_id: AgentId
+    team_manager_id: Optional[str]
 
 
 class TerminalSteps(Mapping):
      across simulation steps.
     """
 
-    def __init__(self, obs, reward, interrupted, agent_id):
+    def __init__(self, obs, reward, interrupted, agent_id, team_manager_id=None):
+        self.team_manager_id: Optional[List[str]] = team_manager_id
 
     @property
     def agent_id_to_index(self) -> Dict[AgentId, int]:
         agent_obs = []
         for batched_obs in self.obs:
             agent_obs.append(batched_obs[agent_index])
+        team_manager_id = None
+        if self.team_manager_id is not None and self.team_manager_id != "":
+            team_manager_id = self.team_manager_id[agent_index]
+            team_manager_id=team_manager_id,
         )
 
     def __iter__(self) -> Iterator[Any]:
             reward=np.zeros(0, dtype=np.float32),
             interrupted=np.zeros(0, dtype=np.bool),
             agent_id=np.zeros(0, dtype=np.int32),
+            team_manager_id=None,
         )
 
 

ml-agents-envs/mlagents_envs/communicator_objects/agent_info_pb2.py

   name='mlagents_envs/communicator_objects/agent_info.proto',
   package='communicator_objects',
   syntax='proto3',
-  serialized_pb=_b('\n3mlagents_envs/communicator_objects/agent_info.proto\x12\x14\x63ommunicator_objects\x1a\x34mlagents_envs/communicator_objects/observation.proto\"\xd1\x01\n\x0e\x41gentInfoProto\x12\x0e\n\x06reward\x18\x07 \x01(\x02\x12\x0c\n\x04\x64one\x18\x08 \x01(\x08\x12\x18\n\x10max_step_reached\x18\t \x01(\x08\x12\n\n\x02id\x18\n \x01(\x05\x12\x13\n\x0b\x61\x63tion_mask\x18\x0b \x03(\x08\x12<\n\x0cobservations\x18\r \x03(\x0b\x32&.communicator_objects.ObservationProtoJ\x04\x08\x01\x10\x02J\x04\x08\x02\x10\x03J\x04\x08\x03\x10\x04J\x04\x08\x04\x10\x05J\x04\x08\x05\x10\x06J\x04\x08\x06\x10\x07J\x04\x08\x0c\x10\rB%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
+  serialized_pb=_b('\n3mlagents_envs/communicator_objects/agent_info.proto\x12\x14\x63ommunicator_objects\x1a\x34mlagents_envs/communicator_objects/observation.proto\"\xea\x01\n\x0e\x41gentInfoProto\x12\x0e\n\x06reward\x18\x07 \x01(\x02\x12\x0c\n\x04\x64one\x18\x08 \x01(\x08\x12\x18\n\x10max_step_reached\x18\t \x01(\x08\x12\n\n\x02id\x18\n \x01(\x05\x12\x13\n\x0b\x61\x63tion_mask\x18\x0b \x03(\x08\x12<\n\x0cobservations\x18\r \x03(\x0b\x32&.communicator_objects.ObservationProto\x12\x17\n\x0fteam_manager_id\x18\x0e \x01(\tJ\x04\x08\x01\x10\x02J\x04\x08\x02\x10\x03J\x04\x08\x03\x10\x04J\x04\x08\x04\x10\x05J\x04\x08\x05\x10\x06J\x04\x08\x06\x10\x07J\x04\x08\x0c\x10\rB%\xaa\x02\"Unity.MLAgents.CommunicatorObjectsb\x06proto3')
   ,
   dependencies=[mlagents__envs_dot_communicator__objects_dot_observation__pb2.DESCRIPTOR,])
 
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
       options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='team_manager_id', full_name='communicator_objects.AgentInfoProto.team_manager_id', index=6,
+      number=14, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
   oneofs=[
   ],
   serialized_start=132,
-  serialized_end=341,
+  serialized_end=366,
 )
 
 _AGENTINFOPROTO.fields_by_name['observations'].message_type = mlagents__envs_dot_communicator__objects_dot_observation__pb2._OBSERVATIONPROTO

ml-agents-envs/mlagents_envs/communicator_objects/agent_info_pb2.pyi

 from typing import (
     Iterable as typing___Iterable,
     Optional as typing___Optional,
+    Text as typing___Text,
 )
 
 from typing_extensions import (
     max_step_reached = ... # type: builtin___bool
     id = ... # type: builtin___int
     action_mask = ... # type: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___bool]
+    team_manager_id = ... # type: typing___Text
 
     @property
     def observations(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[mlagents_envs___communicator_objects___observation_pb2___ObservationProto]: ...
         id : typing___Optional[builtin___int] = None,
         action_mask : typing___Optional[typing___Iterable[builtin___bool]] = None,
         observations : typing___Optional[typing___Iterable[mlagents_envs___communicator_objects___observation_pb2___ObservationProto]] = None,
+        team_manager_id : typing___Optional[typing___Text] = None,
         ) -> None: ...
     @classmethod
     def FromString(cls, s: builtin___bytes) -> AgentInfoProto: ...
-        def ClearField(self, field_name: typing_extensions___Literal[u"action_mask",u"done",u"id",u"max_step_reached",u"observations",u"reward"]) -> None: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"action_mask",u"done",u"id",u"max_step_reached",u"observations",u"reward",u"team_manager_id"]) -> None: ...
-        def ClearField(self, field_name: typing_extensions___Literal[u"action_mask",b"action_mask",u"done",b"done",u"id",b"id",u"max_step_reached",b"max_step_reached",u"observations",b"observations",u"reward",b"reward"]) -> None: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"action_mask",b"action_mask",u"done",b"done",u"id",b"id",u"max_step_reached",b"max_step_reached",u"observations",b"observations",u"reward",b"reward",u"team_manager_id",b"team_manager_id"]) -> None: ...

ml-agents-envs/mlagents_envs/rpc_utils.py

     decision_rewards = np.array(
         [agent_info.reward for agent_info in decision_agent_info_list], dtype=np.float32
     )
+    decision_team_manager = [
+        agent_info.team_manager_id
+        for agent_info in decision_agent_info_list
+        if agent_info.team_manager_id is not None
+    ]
+    if len(decision_team_manager) == 0:
+        decision_team_manager = None
+
+    terminal_team_manager = [
+        agent_info.team_manager_id
+        for agent_info in terminal_agent_info_list
+        if agent_info.team_manager_id is not None
+    ]
+    if len(terminal_team_manager) == 0:
+        terminal_team_manager = None
 
     _raise_on_nan_and_inf(decision_rewards, "rewards")
     _raise_on_nan_and_inf(terminal_rewards, "rewards")
             action_mask = np.split(action_mask, indices, axis=1)
     return (
         DecisionSteps(
-            decision_obs_list, decision_rewards, decision_agent_id, action_mask
+            decision_obs_list,
+            decision_rewards,
+            decision_agent_id,
+            action_mask,
+            decision_team_manager,
-        TerminalSteps(terminal_obs_list, terminal_rewards, max_step, terminal_agent_id),
+        TerminalSteps(
+            terminal_obs_list,
+            terminal_rewards,
+            max_step,
+            terminal_agent_id,
+            terminal_team_manager,
+        ),
     )
 
 

ml-agents/mlagents/trainers/agent_processor.py

         self.experience_buffers: Dict[str, List[AgentExperience]] = defaultdict(list)
         self.last_experience: Dict[str, AgentExperience] = {}
         self.last_step_result: Dict[str, Tuple[DecisionStep, int]] = {}
-        # current_obs is used to collect the last seen obs of all the agents, and assemble the next_collab_obs.
-        self.current_obs: Dict[str, List[np.ndarray]] = {}
+        # current_group_obs is used to collect the last seen obs of all the agents in the same group,
+        # and assemble the next_collab_obs.
+        self.current_group_obs: Dict[str, Dict[str, List[np.ndarray]]] = defaultdict(
+            lambda: defaultdict(list)
+        )
         # last_take_action_outputs stores the action a_t taken before the current observation s_(t+1), while
         # grabbing previous_action from the policy grabs the action PRIOR to that, a_(t-1).
         self.last_take_action_outputs: Dict[str, ActionInfoOutputs] = {}
             local_id = terminal_step.agent_id
             global_id = get_global_agent_id(worker_id, local_id)
             self._assemble_trajectory(terminal_step, global_id)
-        self.current_obs.clear()
+        self.current_group_obs.clear()
 
         # Clean the last experience dictionary for terminal steps
         for terminal_step in terminal_steps.values():
             local_id = ongoing_step.agent_id
             global_id = get_global_agent_id(worker_id, local_id)
             self._assemble_trajectory(ongoing_step, global_id)
-        self.current_obs.clear()
+        self.current_group_obs.clear()
 
         for _gid in action_global_agent_ids:
             # If the ID doesn't have a last step result, the agent just reset,
                 interrupted=interrupted,
                 memory=memory,
             )
-            self.current_obs[global_id] = step.obs
+            if step.team_manager_id is not None:
+                self.current_group_obs[step.team_manager_id][global_id] += step.obs
             self.last_experience[global_id] = experience
 
     def _assemble_trajectory(
             ):
                 next_obs = step.obs
                 next_collab_obs = []
-                for _id, _exp in self.current_obs.items():
-                    if _id == global_id:
-                        continue
-                    else:
+                for _id, _exp in self.current_group_obs[step.team_manager_id].items():
+                    if _id != global_id:
                         next_collab_obs.append(_exp)
 
                 trajectory = Trajectory(

protobuf-definitions/proto/mlagents_envs/communicator_objects/agent_info.proto

     repeated bool action_mask = 11;
     reserved 12; // deprecated CustomObservationProto custom_observation = 12;
     repeated ObservationProto observations = 13;
+    string team_manager_id = 14;
 }

Project/Assets/ML-Agents/Examples/Hallway/Scripts/HallwayTeamManager.cs

+using System.Collections.Generic;
+using Unity.MLAgents;
+using Unity.MLAgents.Extensions.Teams;
+using Unity.MLAgents.Sensors;
+
+public class HallwayTeamManager : BaseTeamManager
+{
+    List<Agent> m_AgentList = new List<Agent> { };
+
+
+    public override void RegisterAgent(Agent agent)
+    {
+        m_AgentList.Add(agent);
+    }
+
+    public override void OnAgentDone(Agent agent, Agent.DoneReason doneReason, List<ISensor> sensors)
+    {
+        agent.SendDoneToTrainer();
+    }
+
+    public override void AddTeamReward(float reward)
+    {
+
+    }
+}

Project/Assets/ML-Agents/Examples/Hallway/Scripts/HallwayTeamManager.cs.meta

+fileFormatVersion: 2
+guid: 8b67166b7adef46febf8b570f92c400d
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

com.unity.ml-agents.extensions/Runtime/Teams.meta

+fileFormatVersion: 2
+guid: 77124df6c18c4f669052016b3116147e
+timeCreated: 1610064454

com.unity.ml-agents/Runtime/ITeamManager.cs

+using System.Collections.Generic;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents
+{
+    public interface ITeamManager
+    {
+        string GetId();
+
+        void RegisterAgent(Agent agent);
+        // TODO not sure this is all the info we need, maybe pass a class/struct instead.
+        void OnAgentDone(Agent agent, Agent.DoneReason doneReason, List<ISensor> sensors);
+    }
+}

com.unity.ml-agents/Runtime/ITeamManager.cs.meta

+fileFormatVersion: 2
+guid: 75810d91665e4477977eb78c9b15aeb3
+timeCreated: 1610057818

com.unity.ml-agents.extensions/Runtime/Teams/BaseTeamManager.cs

+using System.Collections.Generic;
+using Unity.MLAgents;
+using Unity.MLAgents.Sensors;
+
+namespace Unity.MLAgents.Extensions.Teams
+{
+    public class BaseTeamManager : ITeamManager
+    {
+        readonly string m_Id = System.Guid.NewGuid().ToString();
+
+        public virtual void RegisterAgent(Agent agent)
+        {
+            throw new System.NotImplementedException();
+        }
+
+        public virtual void OnAgentDone(Agent agent, Agent.DoneReason doneReason, List<ISensor> sensors)
+        {
+            // Possible implementation - save reference to Agent's IPolicy so that we can repeatedly
+            // call IPolicy.RequestDecision on behalf of the Agent after it's dead
+            // If so, we'll need dummy sensor impls with the same shape as the originals.
+            throw new System.NotImplementedException();
+        }
+
+        public virtual void AddTeamReward(float reward)
+        {
+
+        }
+
+        public string GetId()
+        {
+            return m_Id;
+        }
+
+    }
+}

com.unity.ml-agents.extensions/Runtime/Teams/BaseTeamManager.cs.meta

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