using System ;
using System.Collections.Generic ;
using System.Collections.ObjectModel ;
using UnityEngine.Serialization ;
namespace MLAgents
{
public float [ ] storedVectorActions ;
/// <summary>
/// For discrete control, specifies the actions that the agent cannot take. Is true if
/// the action is mask ed.
/// For discrete control, specifies the actions that the agent cannot take.
/// An element of the mask array is <c>true</c> if the action is prohibit ed.
/// C urrent agent reward.
/// The c urrent agent reward.
/// </summary>
public float reward ;
}
/// <summary>
/// Agent MonoBehaviour class that is attached to a Unity GameObject, making it
/// an Agent. An agent produces observations and takes actions in the
/// environment. Observations are determined by the cameras attached
/// to the agent in addition to the vector observations implemented by the
/// user in <see cref="Agent.CollectObservations(VectorSensor)"/>.
/// On the other hand, actions are determined by decisions produced by a Policy.
/// Currently, this class is expected to be extended to implement the desired agent behavior.
/// An agent is an actor that can observe its environment, decide on the
/// best course of action using those observations, and execute those actions
/// within the environment.
/// Simply speaking, an agent roams through an environment and at each step
/// of the environment extracts its current observation, sends them to its
/// policy and in return receives an action. In practice,
/// however, an agent need not send its observation at every step since very
/// little may have changed between successive steps.
/// Use the Agent class as the subclass for implementing your own agents. Add
/// your Agent implementation to a [GameObject] in the [Unity scene] that serves
/// as the agent's environment.
/// At any step, an agent may be considered done due to a variety of reasons:
/// - The agent reached an end state within its environment.
/// - The agent reached the maximum # of steps (i.e. timed out).
/// - The academy reached the maximum # of steps (forced agent to be done).
/// Agents in an environment operate in *steps*. At each step, an agent collects observations,
/// passes them to its decision-making policy, and receives an action vector in response.
/// Here, an agent reaches an end state if it completes its task successfully
/// or somehow fails along the way. In the case where an agent is done before
/// the academy, it either resets and restarts, or just lingers until the
/// academy is done.
/// Agents make observations using <see cref="ISensor"/> implementations. The ML-Agents
/// API provides implementations for visual observations (<see cref="CameraSensor"/>)
/// raycast observations (<see cref="RayPerceptionSensor"/>), and arbitrary
/// data observations (<see cref="VectorSensor"/>). You can add the
/// <see cref="CameraSensorComponent"/> and <see cref="RayPerceptionSensorComponent2D"/> or
/// <see cref="RayPerceptionSensorComponent3D"/> components to an agent's [GameObject] to use
/// those sensor types. You can implement the <see cref="CollectObservations(VectorSensor)"/>
/// function in your Agent subclass to use a vector observation. The Agent class calls this
/// function before it uses the observation vector to make a decision. (If you only use
/// visual or raycast observations, you do not need to implement
/// <see cref="CollectObservations"/>.)
/// An important note regarding steps and episodes is due. Here, an agent step
/// corresponds to an academy step, which also corresponds to Unity
/// environment step (i.e. each FixedUpdate call). This is not the case for
/// episodes. The academy controls the global episode count and each agent
/// controls its own local episode count and can reset and start a new local
/// episode independently (based on its own experience). Thus an academy
/// (global) episode can be viewed as the upper-bound on an agents episode
/// length and that within a single global episode, an agent may have completed
/// multiple local episodes. Consequently, if an agent max step is
/// set to a value larger than the academy max steps value, then the academy
/// value takes precedence (since the agent max step will never be reached).
/// Assign a decision making policy to an agent using a <see cref="BehaviorParameters"/>
/// component attached to the agent's [GameObject]. The <see cref="BehaviorType"/> setting
/// determines how decisions are made:
/// Lastly, note that at any step the policy to the agent is allowed to
/// change model with <see cref="SetModel"/>.
/// * <see cref="BehaviorType.Default"/>: decisions are made by the external process,
/// when connected. Otherwise, decisions are made using inference. If no inference model
/// is specified in the BehaviorParameters component, then heuristic decision
/// making is used.
/// * <see cref="BehaviorType.InferenceOnly"/>: decisions are always made using the trained
/// model specified in the <see cref="BehaviorParameters"/> component.
/// * <see cref="BehaviorType.HeuristicOnly"/>: when a decision is needed, the agent's
/// <see cref="Heuristic"/> function is called. Your implementation is responsible for
/// providing the appropriate action.
/// Implementation-wise, it is required that this class is extended and the
/// virtual methods overridden. For sample implementations of agent behavior,
/// see the Examples/ directory within this Unity project.
/// To trigger an agent decision automatically, you can attach a <see cref="DecisionRequester"/>
/// component to the Agent game object. You can also call the agent's <see cref="RequestDecision"/>
/// function manually. You only need to call <see cref="RequestDecision"/> when the agent is
/// in a position to act upon the decision. In many cases, this will be every [FixedUpdate]
/// callback, but could be less frequent. For example, an agent that hops around its environment
/// can only take an action when it touches the ground, so several frames might elapse between
/// one decision and the need for the next.
///
/// Use the <see cref="OnActionReceived"/> function to implement the actions your agent can take,
/// such as moving to reach a goal or interacting with its environment.
///
/// When you call <see cref="EndEpisode"/> on an agent or the agent reaches its <see cref="maxStep"/> count,
/// its current episode ends. You can reset the agent -- or remove it from the
/// environment -- by implementing the <see cref="OnEpisodeBegin"/> function. An agent also
/// becomes done when the <see cref="Academy"/> resets the environment, which only happens when
/// the <see cref="Academy"/> receives a reset signal from an external process via the
/// <see cref="Academy.Communicator"/>.
///
/// The Agent class extends the Unity [MonoBehaviour] class. You can implement the
/// standard [MonoBehaviour] functions as needed for your agent. Since an agent's
/// observations and actions typically take place during the [FixedUpdate] phase, you should
/// only use the [MonoBehaviour.Update] function for cosmetic purposes. If you override the [MonoBehaviour]
/// methods, [OnEnable()] or [OnDisable()], always call the base Agent class implementations.
///
/// You can implement the <see cref="Heuristic"/> function to specify agent actions using
/// your own heuristic algorithm. Implementing a heuristic function can be useful
/// for debugging. For example, you can use keyboard input to select agent actions in
/// order to manually control an agent's behavior.
///
/// Note that you can change the inference model assigned to an agent at any step
/// by calling <see cref="SetModel"/>.
///
/// See [Agents] and [Reinforcement Learning in Unity] in the [Unity ML-Agents Toolkit manual] for
/// more information on creating and training agents.
///
/// For sample implementations of agent behavior, see the examples available in the
/// [Unity ML-Agents Toolkit] on Github.
///
/// [MonoBehaviour]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.html
/// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
/// [Unity scene]: https://docs.unity3d.com/Manual/CreatingScenes.html
/// [FixedUpdate]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.FixedUpdate.html
/// [MonoBehaviour.Update]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.Update.html
/// [OnEnable()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnEnable.html
/// [OnDisable()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnDisable.html]
/// [OnBeforeSerialize()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnBeforeSerialize.html
/// [OnAfterSerialize()]: https://docs.unity3d.com/ScriptReference/MonoBehaviour.OnAfterSerialize.html
/// [Agents]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Learning-Environment-Design-Agents.md
/// [Reinforcement Learning in Unity]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Learning-Environment-Design.md
/// [Unity ML-Agents Toolkit]: https://github.com/Unity-Technologies/ml-agents
/// [Unity ML-Agents Toolkit manual]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Readme.md
///
/// </remarks>
[ HelpURL ( "https://github.com/Unity-Technologies/ml-agents/blob/master/" +
"docs/Learning-Environment-Design-Agents.md" ) ]
IPolicy m_Brain ;
BehaviorParameters m_PolicyFactory ;
/// This code is here to make the upgrade path for users using m axStep
/// This code is here to make the upgrade path for users using M axStep
/// easier. We will hook into the Serialization code and make sure that
/// agentParameters.maxStep and this.maxStep are in sync.
[Serializable]
/// <summary>
/// The maximum number of steps the agent takes before being done.
/// </summary>
/// <value>The maximum steps for an agent to take before it resets; or 0 for
/// unlimited steps.</value>
/// If set to 0, the agent can only be set to done programmatically (or
/// when the Academy is done).
/// If set to any positive integer, the agent will be set to done after
/// that many steps. Note that setting the max step to a value greater
/// than the academy max step value renders it useless.
/// The max step value determines the maximum length of an agent's episodes.
/// Set to a positive integer to limit the episode length to that many steps.
/// Set to 0 for unlimited episode length.
///
/// When an episode ends and a new one begins, the Agent object's
/// <seealso cref="OnEpisodeBegin"/> function is called. You can implement
/// <see cref="OnEpisodeBegin"/> to reset the agent or remove it from the
/// environment. An agent's episode can also end if you call its <seealso cref="EndEpisode"/>
/// method or an external process resets the environment through the <see cref="Academy"/>.
///
/// Consider limiting the number of steps in an episode to avoid wasting time during
/// training. If you set the max step value to a reasonable estimate of the time it should
/// take to complete a task, then agents that haven’t succeeded in that time frame will
/// reset and start a new training episode rather than continue to fail.
[HideInInspector] public int maxStep ;
/// <example>
/// To use a step limit when training while allowing agents to run without resetting
/// outside of training, you can set the max step to 0 in <see cref="Initialize"/>
/// if the <see cref="Academy"/> is not connected to an external process.
/// <code>
/// using MLAgents;
///
/// public class MyAgent : Agent
/// {
/// public override void Initialize()
/// {
/// if (!Academy.Instance.IsCommunicatorOn)
/// {
/// this.MaxStep = 0;
/// }
/// }
/// }
/// </code>
/// **Note:** in general, you should limit the differences between the code you execute
/// during training and the code you run during inference.
/// </example>
[FormerlySerializedAs("maxStep")]
[HideInInspector] public int MaxStep ;
/// Current Agent information (message sent to Brain).
AgentInfo m_Info ;
/// <summary>
/// Called when the attached <see cref="GameObject"/> becomes enabled and active.
/// </summary>
/// <remarks>
/// This function initializes the Agent instance, if it hasn't been initialized yet.
/// Always call the base Agent class version of this function if you implement `OnEnable()`
/// in your own Agent subclasses.
/// </remarks>
/// <example>
/// <code>
/// protected override void OnEnable()
/// {
/// base.OnEnable();
/// // additional OnEnable logic...
/// }
/// </code>
/// </example>
protected virtual void OnEnable ( )
{
LazyInitialize ( ) ;
/// <inheritdoc cref="OnBeforeSerialize"/>
/// Called by Unity immediately before serializing this object.
/// <remarks>
/// The Agent class uses OnBeforeSerialize() for internal housekeeping. Call the
/// base class implementation if you need your own custom serialization logic.
///
/// See [OnBeforeSerialize] for more information.
///
/// [OnBeforeSerialize]: https://docs.unity3d.com/ScriptReference/ISerializationCallbackReceiver.OnAfterDeserialize.html
/// </remarks>
/// <example>
/// <code>
/// public new void OnBeforeSerialize()
/// {
/// base.OnBeforeSerialize();
/// // additional serialization logic...
/// }
/// </code>
/// </example>
// Manages a serialization upgrade issue from v0.13 to v0.14 where maxStep moved
// Manages a serialization upgrade issue from v0.13 to v0.14 where MaxStep moved
if ( maxStep = = 0 & & maxStep ! = agentParameters . maxStep & & ! hasUpgradedFromAgentParameters )
if ( MaxStep = = 0 & & MaxStep ! = agentParameters . maxStep & & ! hasUpgradedFromAgentParameters )
maxStep = agentParameters . maxStep ;
MaxStep = agentParameters . maxStep ;
/// <inheritdoc cref="OnAfterDeserialize"/>
/// Called by Unity immediately after deserializing this object.
/// <remarks>
/// The Agent class uses OnAfterDeserialize() for internal housekeeping. Call the
/// base class implementation if you need your own custom deserialization logic.
///
/// See [OnAfterDeserialize] for more information.
///
/// [OnAfterDeserialize]: https://docs.unity3d.com/ScriptReference/ISerializationCallbackReceiver.OnAfterDeserialize.html
/// </remarks>
/// <example>
/// <code>
/// public new void OnAfterDeserialize()
/// {
/// base.OnAfterDeserialize();
/// // additional deserialization logic...
/// }
/// </code>
/// </example>
// Manages a serialization upgrade issue from v0.13 to v0.14 where maxStep moved
// Manages a serialization upgrade issue from v0.13 to v0.14 where MaxStep moved
if ( maxStep = = 0 & & maxStep ! = agentParameters . maxStep & & ! hasUpgradedFromAgentParameters )
if ( MaxStep = = 0 & & MaxStep ! = agentParameters . maxStep & & ! hasUpgradedFromAgentParameters )
maxStep = agentParameters . maxStep ;
MaxStep = agentParameters . maxStep ;
}
hasUpgradedFromAgentParameters = true ;
}
/// </summary>
/// <remarks>
/// This function calls your <seealso cref="Initialize"/> implementation, if one exists.
/// </remarks>
public void LazyInitialize ( )
{
if ( m_Initialized )
}
/// <summary>
/// Reason that the Agent is being considered "done"
/// The reason that the Agent has been set to "done".
/// The <see cref="Don e"/> method was called.
/// The <see cref="EndEpisod e"/> method was called.
/// </summary>
DoneCalled ,
MaxStepReached ,
/// <summary>
/// The Agent was disabled
/// The Agent was disabled.
/// </summary>
Disabled ,
}
/// </summary>
/// <remarks>
/// Always call the base Agent class version of this function if you implement `OnDisable()`
/// in your own Agent subclasses.
/// </remarks>
/// <example>
/// <code>
/// protected override void OnDisable()
/// {
/// base.OnDisable();
/// // additional OnDisable logic...
/// }
/// </code>
/// </example>
/// <seealso cref="OnEnable"/>
protected virtual void OnDisable ( )
{
DemonstrationWriters . Clear ( ) ;
}
/// <summary>
/// Updates the Model for the agent. Any model currently assigned to the
/// agent will be replaced with the provided one. If the arguments are
/// identical to the current parameters of the agent, the model will
/// remain unchanged.
/// Updates the Model assigned to this Agent instance.
/// <remarks>
/// If the agent already has an assigned model, that model is replaced with the
/// the provided one. However, if you call this function with arguments that are
/// identical to the current parameters of the agent, then no changes are made.
///
/// **Note:** the <paramref name="behaviorName"/> parameter is ignored when not training.
/// The <paramref name="model"/> and <paramref name="inferenceDevice"/> parameters
/// are ignored when not using inference.
/// </remarks>
/// <param name = "inferenceDevice"> Define on what device the model
/// <param name = "inferenceDevice"> Define the device on which the model
/// will be run.</param>
public void SetModel (
string behaviorName ,
if ( behaviorName = = m_PolicyFactory . behaviorName & &
model = = m_PolicyFactory . model & &
inferenceDevice = = m_PolicyFactory . inferenceDevice )
if ( behaviorName = = m_PolicyFactory . BehaviorName & &
model = = m_PolicyFactory . Model & &
inferenceDevice = = m_PolicyFactory . InferenceDevice )
m_PolicyFactory . model = model ;
m_PolicyFactory . inferenceDevice = inferenceDevice ;
m_PolicyFactory . behaviorName = behaviorName ;
m_PolicyFactory . Model = model ;
m_PolicyFactory . InferenceDevice = inferenceDevice ;
m_PolicyFactory . BehaviorName = behaviorName ;
ReloadPolicy ( ) ;
}
/// Overrides the current step reward of the agent and updates the episode
/// reward accordingly.
/// </summary>
/// <remarks>
/// This function replaces any rewards given to the agent during the current step.
/// Use <see cref="AddReward(float)"/> to incrementally change the reward rather than
/// overriding it.
///
/// Typically, you assign rewards in the Agent subclass's <see cref="OnActionReceived(float[])"/>
/// implementation after carrying out the received action and evaluating its success.
///
/// Rewards are used during reinforcement learning; they are ignored during inference.
///
/// See [Agents - Rewards] for general advice on implementing rewards and [Reward Signals]
/// for information about mixing reward signals from curiosity and Generative Adversarial
/// Imitation Learning (GAIL) with rewards supplied through this method.
///
/// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Learning-Environment-Design-Agents.md#rewards
/// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Reward-Signals.md
/// </remarks>
/// <param name="reward">The new value of the reward.</param>
public void SetReward ( float reward )
{
/// <summary>
/// Increments the step and episode rewards by the provided value.
/// </summary>
/// <remarks>Use a positive reward to reinforce desired behavior. You can use a
/// negative reward to penalize mistakes. Use <seealso cref="SetReward(float)"/> to
/// set the reward assigned to the current step with a specific value rather than
/// increasing or decreasing it.
///
/// Typically, you assign rewards in the Agent subclass's <see cref="OnActionReceived(float[])"/>
/// implementation after carrying out the received action and evaluating its success.
///
/// Rewards are used during reinforcement learning; they are ignored during inference.
///
/// See [Agents - Rewards] for general advice on implementing rewards and [Reward Signals]
/// for information about mixing reward signals from curiosity and Generative Adversarial
/// Imitation Learning (GAIL) with rewards supplied through this method.
///
/// [Agents - Rewards]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Learning-Environment-Design-Agents.md#rewards
/// [Reward Signals]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Reward-Signals.md
///</remarks>
/// <param name="increment">Incremental reward value.</param>
public void AddReward ( float increment )
{
void UpdateRewardStats ( )
{
var gaugeName = $"{m_PolicyFactory.behaviorName}.CumulativeReward" ;
var gaugeName = $"{m_PolicyFactory.BehaviorName}.CumulativeReward" ;
/// Sets the done flag to true.
/// Sets the done flag to true and resets the agent.
/// <seealso cref="OnEpisodeBegin"/>
public void EndEpisode ( )
{
NotifyAgentDone ( DoneReason . DoneCalled ) ;
/// <summary>
/// Is called when the agent must request the brain for a new decision.
/// Requests a new decision for this agent.
/// <remarks>
/// Call `RequestDecision()` whenever an agent needs a decision. You often
/// want to request a decision every environment step. However, if an agent
/// cannot use the decision every step, then you can request a decision less
/// frequently.
///
/// You can add a <seealso cref="DecisionRequester"/> component to the agent's
/// [GameObject] to drive the agent's decision making. When you use this component,
/// do not call `RequestDecision()` separately.
///
/// Note that this function calls <seealso cref="RequestAction"/>; you do not need to
/// call both functions at the same time.
///
/// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
/// </remarks>
public void RequestDecision ( )
{
m_RequestDecision = true ;
/// <summary>
/// Is called then the agent must perform a new action.
/// Requests an action for this agent.
/// <remarks>
/// Call `RequestAction()` to repeat the previous action returned by the agent's
/// most recent decision. A new decision is not requested. When you call this function,
/// the Agent instance invokes <seealso cref="OnActionReceived(float[])"/> with the
/// existing action vector.
///
/// You can use `RequestAction()` in situations where an agent must take an action
/// every update, but doesn't need to make a decision as often. For example, an
/// agent that moves through its environment might need to apply an action to keep
/// moving, but only needs to make a decision to change course or speed occasionally.
///
/// You can add a <seealso cref="DecisionRequester"/> component to the agent's
/// [GameObject] to drive the agent's decision making and action frequency. When you
/// use this component, do not call `RequestAction()` separately.
///
/// Note that <seealso cref="RequestDecision"/> calls `RequestAction()`; you do not need to
/// call both functions at the same time.
///
/// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
/// </remarks>
public void RequestAction ( )
{
m_RequestAction = true ;
/// at the end of an episode.
void ResetData ( )
{
var param = m_PolicyFactory . b rainParameters;
var param = m_PolicyFactory . B rainParameters;
m_ActionMasker = new DiscreteActionMasker ( param ) ;
// If we haven't initialized vectorActions, initialize to 0. This should only
// happen during the creation of the Agent. In subsequent episodes, vectorAction
m_Action . vectorActions = new float [ param . numActions ] ;
m_Info . storedVectorActions = new float [ param . numActions ] ;
m_Action . vectorActions = new float [ param . NumActions ] ;
m_Info . storedVectorActions = new float [ param . NumActions ] ;
/// Initializes the agent, called once when the agent is enabled. Can be
/// left empty if there is no special, unique set-up behavior for the
/// agent.
/// Implement `Initialize()` to perform one-time initialization or set up of the
/// Agent instance.
/// One sample use is to store local references to other objects in the
/// scene which would facilitate computing this agents observation.
/// `Initialize()` is called once when the agent is first enabled. If, for example,
/// the Agent object needs references to other [GameObjects] in the scene, you
/// can collect and store those references here.
///
/// Note that <seealso cref="OnEpisodeBegin"/> is called at the start of each of
/// the agent's "episodes". You can use that function for items that need to be reset
/// for each episode.
///
/// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
public virtual void Initialize ( ) { }
public virtual void Initialize ( ) { }
/// When the Agent uses Heuristics, it will call this method every time it
/// needs an action. This can be used for debugging or controlling the agent
/// with keyboard. This can also be useful to record demonstrations for imitation learning.
/// Implement `Heuristic()` to choose an action for this agent using a custom heuristic.
/// <param name="actionsOut">An array corresponding to the next action of the Agent</param>
/// <remarks>
/// Implement this function to provide custom decision making logic or to support manual
/// control of an agent using keyboard, mouse, or game controller input.
///
/// Your heuristic implementation can use any decision making logic you specify. Assign decision
/// values to the float[] array, <paramref cref="actionsOut"/>, passed to your function as a parameter.
/// Add values to the array at the same indexes as they are used in your
/// <seealso cref="OnActionReceived(float[])"/> function, which receives this array and
/// implements the corresponding agent behavior. See [Actions] for more information
/// about agent actions.
///
/// An agent calls this `Heuristic()` function to make a decision when you set its behavior
/// type to <see cref="BehaviorType.HeuristicOnly"/>. The agent also calls this function if
/// you set its behavior type to <see cref="BehaviorType.Default"/> when the
/// <see cref="Academy"/> is not connected to an external training process and you do not
/// assign a trained model to the agent.
///
/// To perform imitation learning, implement manual control of the agent in the `Heuristic()`
/// function so that you can record the demonstrations required for the imitation learning
/// algorithms. (Attach a [Demonstration Recorder] component to the agent's [GameObject] to
/// record the demonstration session to a file.)
///
/// Even when you don’t plan to use heuristic decisions for an agent or imitation learning,
/// implementing a simple heuristic function can aid in debugging agent actions and interactions
/// with its environment.
///
/// [Demonstration Recorder]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Training-Imitation-Learning.md#recording-demonstrations
/// [Actions]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Learning-Environment-Design-Agents.md#actions
/// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
/// </remarks>
/// <example>
/// The following example illustrates a `Heuristic()` function that provides WASD-style
/// keyboard control for an agent that can move in two dimensions as well as jump. See
/// [Input Manager] for more information about the built-in Unity input functions.
/// You can also use the [Input System package], which provides a more flexible and
/// configurable input system.
/// <code>
/// public override void Heuristic(float[] actionsOut)
/// {
/// actionsOut[0] = Input.GetAxis("Horizontal");
/// actionsOut[1] = Input.GetKey(KeyCode.Space) ? 1.0f : 0.0f;
/// actionsOut[2] = Input.GetAxis("Vertical");
/// }
/// </code>
/// [Input Manager]: https://docs.unity3d.com/Manual/class-InputManager.html
/// [Input System package]: https://docs.unity3d.com/Packages/com.unity.inputsystem@1.0/manual/index.html
/// </example>
/// <param name="actionsOut">Array for the output actions.</param>
/// <seealso cref="OnActionReceived(float[])"/>
public virtual void Heuristic ( float [ ] actionsOut )
{
Debug . LogWarning ( "Heuristic method called but not implemented. Returning placeholder actions." ) ;
{
// Get all attached sensor components
SensorComponent [ ] attachedSensorComponents ;
if ( m_PolicyFactory . u seChildSensors)
if ( m_PolicyFactory . U seChildSensors)
{
attachedSensorComponents = GetComponentsInChildren < SensorComponent > ( ) ;
}
}
// Support legacy CollectObservations
var param = m_PolicyFactory . b rainParameters;
if ( param . v ectorObservationSize> 0 )
var param = m_PolicyFactory . B rainParameters;
if ( param . V ectorObservationSize> 0 )
collectObservationsSensor = new VectorSensor ( param . v ectorObservationSize) ;
if ( param . n umStackedVectorObservations> 1 )
collectObservationsSensor = new VectorSensor ( param . V ectorObservationSize) ;
if ( param . N umStackedVectorObservations> 1 )
collectObservationsSensor , param . n umStackedVectorObservations) ;
collectObservationsSensor , param . N umStackedVectorObservations) ;
sensors . Add ( stackingSensor ) ;
}
else
}
using ( TimerStack . Instance . Scoped ( "CollectDiscreteActionMasks" ) )
{
if ( m_PolicyFactory . brainParameters . v ectorActionSpaceType= = SpaceType . Discrete )
if ( m_PolicyFactory . BrainParameters . V ectorActionSpaceType= = SpaceType . Discrete )
{
CollectDiscreteActionMasks ( m_ActionMasker ) ;
}
}
/// <summary>
/// Collects the vector observations of the agent.
/// The agent observation describes the current environment from the
/// perspective of the agent.
/// Implement `CollectObservations()` to collect the vector observations of
/// the agent for the step. The agent observation describes the current
/// environment from the perspective of the agent.
/// An agents observation is any environment information that helps
/// the Agent achieve its goal. For example, for a fighting Agent, its
/// An agent's observation is any environment information that helps
/// the agent achieve its goal. For example, for a fighting agent, its
/// Recall that an Agent may attach vector or visual observations.
/// Vector observations are added by calling the provided helper methods
/// on the VectorSensor input:
///
/// You can use a combination of vector, visual, and raycast observations for an
/// agent. If you only use visual or raycast observations, you do not need to
/// implement a `CollectObservations()` function.
///
/// Add vector observations to the <paramref name="sensor"/> parameter passed to
/// this method by calling the <seealso cref="VectorSensor"/> helper methods:
/// - <see cref="VectorSensor.AddObservation(int)"/>
/// - <see cref="VectorSensor.AddObservation(float)"/>
/// - <see cref="VectorSensor.AddObservation(Vector3)"/>
/// - <see cref="VectorSensor.AddObservation(IEnumerable{float})"/>
/// - <see cref="VectorSensor.AddOneHotObservation(int, int)"/>
/// Depending on your environment, any combination of these helpers can
/// be used. They just need to be used in the exact same order each time
/// this method is called and the resulting size of the vector observation
/// needs to match the vectorObservationSize attribute of the linked Brain.
/// Visual observations are implicitly added from the cameras attached to
/// the Agent.
///
/// You can use any combination of these helper functions to build the agent's
/// vector of observations. You must build the vector in the same order
/// each time `CollectObservations()` is called and the length of the vector
/// must always be the same. In addition, the length of the observation must
/// match the <see cref="BrainParameters.VectorObservationSize"/>
/// attribute of the linked Brain, which is set in the Editor on the
/// **Behavior Parameters** component attached to the agent's [GameObject].
///
/// For more information about observations, see [Observations and Sensors].
///
/// [GameObject]: https://docs.unity3d.com/Manual/GameObjects.html
/// [Observations and Sensors]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Learning-Environment-Design-Agents.md#observations-and-sensors
/// </remarks>
public virtual void CollectObservations ( VectorSensor sensor )
{
/// Collects the masks for discrete actions.
/// When using discrete actions, the agent will not perform the masked action.
/// Returns a read-only view of the observations that were generated in
/// <see cref="CollectObservations(VectorSensor)"/>. This is mainly useful inside of a
/// <see cref="Heuristic(float[])"/> method to avoid recomputing the observations.
/// </summary>
/// <returns>A read-only view of the observations list.</returns>
public ReadOnlyCollection < float > GetObservations ( )
{
return collectObservationsSensor . GetObservations ( ) ;
}
/// <summary>
/// Implement `CollectDiscreteActionMasks()` to collects the masks for discrete
/// actions. When using discrete actions, the agent will not perform the masked
/// action.
/// </summary>
/// <param name="actionMasker">
/// The action masker for the agent.
/// action by masking it with <see cref="DiscreteActionMasker.SetMask(int, IEnumerable{int})"/>
/// action by masking it with <see cref="DiscreteActionMasker.SetMask(int, IEnumerable{int})"/>.
///
/// See [Agents - Actions] for more information on masking actions.
///
/// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Learning-Environment-Design-Agents.md#actions
/// <seealso cref="OnActionReceived(float[])"/>
/// Specifies the agent behavior at every step based on the provided
/// action.
/// Implement `OnActionReceived()` to specify agent behavior at every step, based
/// on the provided action.
/// <remarks>
/// An action is passed to this function in the form of an array vector. Your
/// implementation must use the array to direct the agent's behavior for the
/// current step.
///
/// You decide how many elements you need in the action array to control your
/// agent and what each element means. For example, if you want to apply a
/// force to move an agent around the environment, you can arbitrarily pick
/// three values in the action array to use as the force components. During
/// training, the agent's policy learns to set those particular elements of
/// the array to maximize the training rewards the agent receives. (Of course,
/// if you implement a <seealso cref="Heuristic"/> function, it must use the same
/// elements of the action array for the same purpose since there is no learning
/// involved.)
///
/// Actions for an agent can be either *Continuous* or *Discrete*. Specify which
/// type of action space an agent uses, along with the size of the action array,
/// in the <see cref="BrainParameters"/> of the agent's associated
/// <see cref="BehaviorParameters"/> component.
///
/// When an agent uses the continuous action space, the values in the action
/// array are floating point numbers. You should clamp the values to the range,
/// -1..1, to increase numerical stability during training.
///
/// When an agent uses the discrete action space, the values in the action array
/// are integers that each represent a specific, discrete action. For example,
/// you could define a set of discrete actions such as:
///
/// <code>
/// 0 = Do nothing
/// 1 = Move one space left
/// 2 = Move one space right
/// 3 = Move one space up
/// 4 = Move one space down
/// </code>
///
/// When making a decision, the agent picks one of the five actions and puts the
/// corresponding integer value in the action vector. For example, if the agent
/// decided to move left, the action vector parameter would contain an array with
/// a single element with the value 1.
///
/// You can define multiple sets, or branches, of discrete actions to allow an
/// agent to perform simultaneous, independent actions. For example, you could
/// use one branch for movement and another branch for throwing a ball left, right,
/// up, or down, to allow the agent to do both in the same step.
///
/// The action vector of a discrete action space contains one element for each
/// branch. The value of each element is the integer representing the chosen
/// action for that branch. The agent always chooses one action for each
/// branch.
///
/// When you use the discrete action space, you can prevent the training process
/// or the neural network model from choosing specific actions in a step by
/// implementing the <see cref="CollectDiscreteActionMasks(DiscreteActionMasker)"/>
/// function. For example, if your agent is next to a wall, you could mask out any
/// actions that would result in the agent trying to move into the wall.
///
/// For more information about implementing agent actions see [Agents - Actions].
///
/// [Agents - Actions]: https://github.com/Unity-Technologies/ml-agents/blob/0.15.1/docs/Learning-Environment-Design-Agents.md#actions
/// </remarks>
/// Vector action. Note that for discrete actions, the provided array
/// will be of length 1.
/// An array containing the action vector. The length of the array is specified
/// by the <see cref="BrainParameters"/> of the agent's associated
/// <see cref="BehaviorParameters"/> component.
public virtual void OnActionReceived ( float [ ] vectorAction ) { }
public virtual void OnActionReceived ( float [ ] vectorAction ) { }
/// Specifies the agent behavior when being reset, which can be due to
/// the agent or Academy being done (i.e. completion of local or global
/// episode).
/// Implement `OnEpisodeBegin()` to set up an Agent instance at the beginning
/// of an episode.
public virtual void OnEpisodeBegin ( ) { }
/// <seealso cref="Initialize"/>
/// <seealso cref="EndEpisode"/>
public virtual void OnEpisodeBegin ( ) { }
/// Returns the last action that was decided on by the Agent
/// Returns the last action that was decided on by the Agent.
/// The last action that was decided by the Agent (or null if no decision has been made)
/// The last action that was decided by the Agent (or null if no decision has been made).
/// <seealso cref="OnActionReceived(float[])"/>
public float [ ] GetAction ( )
{
return m_Action . vectorActions ;
/// An internal reset method that updates internal data structures in
/// addition to calling <see cref="AgentReset "/>.
/// addition to calling <see cref="OnEpisodeBegin "/>.
/// </summary>
void _AgentReset ( )
{
/// <summary>
/// Scales continuous action from [-1, 1] to arbitrary range.
/// </summary>
/// <param name="rawAction"></param>
/// <param name="min"></param>
/// <param name="max"></param>
/// <returns></returns>
/// <param name="rawAction">The input action value.</param>
/// <param name="min">The minimum output value.</param>
/// <param name="max">The maximum output value.</param>
/// <returns>The <paramref name="rawAction"/> scaled from [-1,1] to
/// [<paramref name="min"/>, <paramref name="max"/>].</returns>
protected static float ScaleAction ( float rawAction , float min , float max )
{
var middle = ( min + max ) / 2 ;
/// <summary>
/// Signals the agent that it must sent its decision to the brain.
/// Signals the agent that it must send its decision to the brain.
/// </summary>
void SendInfo ( )
{
OnActionReceived ( m_Action . vectorActions ) ;
}
if ( ( m_StepCount > = m axStep) & & ( m axStep> 0 ) )
if ( ( m_StepCount > = M axStep) & & ( M axStep> 0 ) )
{
NotifyAgentDone ( DoneReason . MaxStepReached ) ;
_AgentReset ( ) ;
Andrew Cohen
4 年前