/// <inheritdoc />
public RemotePolicy(
BrainParameters brainParameters,
string behaviorName)
{
m_BehaviorName = behaviorName;
var aca = Object.FindObjectOfType <Academy>();
aca.LazyInitialization();
m_Communicator = aca.Communicator;
aca.Communicator.SubscribeBrain(m_BehaviorName, brainParameters);
}
/// <summary>
/// Convert a BrainParametersProto to a BrainParameters struct.
/// </summary>
/// <param name="bpp">An instance of a brain parameters protobuf object.</param>
/// <returns>A BrainParameters struct.</returns>
public static BrainParameters ToBrainParameters(this BrainParametersProto bpp)
{
var bp = new BrainParameters
{
vectorActionSize = bpp.VectorActionSize.ToArray(),
vectorActionDescriptions = bpp.VectorActionDescriptions.ToArray(),
vectorActionSpaceType = (SpaceType)bpp.VectorActionSpaceType
};
return(bp);
}
/// <inheritdoc />
public BarracudaPolicy(
BrainParameters brainParameters,
NNModel model,
InferenceDevice inferenceDevice)
{
var aca = Object.FindObjectOfType <Academy>();
aca.LazyInitialization();
var modelRunner = aca.GetOrCreateModelRunner(model, brainParameters, inferenceDevice);
m_ModelRunner = modelRunner;
}
/// <summary>
/// Creates or retrieves an existing ModelRunner that uses the same
/// NNModel and the InferenceDevice as provided.
/// </summary>
/// <param name="model"> The NNModel the ModelRunner must use </param>
/// <param name="brainParameters"> The brainParameters used to create
/// the ModelRunner </param>
/// <param name="inferenceDevice"> The inference device (CPU or GPU)
/// the ModelRunner will use </param>
/// <returns> The ModelRunner compatible with the input settings</returns>
internal ModelRunner GetOrCreateModelRunner(
NNModel model, BrainParameters brainParameters, InferenceDevice inferenceDevice)
{
var modelRunner = m_ModelRunners.Find(x => x.HasModel(model, inferenceDevice));
if (modelRunner == null)
{
modelRunner = new ModelRunner(
model, brainParameters, inferenceDevice);
m_ModelRunners.Add(modelRunner);
}
return(modelRunner);
}
/// <summary>
/// Writes brain parameters to file.
/// </summary>
void WriteBrainParameters(string brainName, BrainParameters brainParameters)
{
if (m_Writer == null)
{
// Already closed
return;
}
// Writes BrainParameters to file.
m_Writer.Seek(MetaDataBytes + 1, 0);
var brainProto = brainParameters.ToProto(brainName, false);
brainProto.WriteDelimitedTo(m_Writer);
}
/// <summary>
/// Converts a Brain into to a Protobuf BrainInfoProto so it can be sent
/// </summary>
/// <returns>The BrainInfoProto generated.</returns>
/// <param name="bp">The instance of BrainParameter to extend.</param>
/// <param name="name">The name of the brain.</param>
/// <param name="isTraining">Whether or not the Brain is training.</param>
public static BrainParametersProto ToProto(this BrainParameters bp, string name, bool isTraining)
{
var brainParametersProto = new BrainParametersProto
{
VectorActionSize = { bp.vectorActionSize },
VectorActionSpaceType =
(SpaceTypeProto)bp.vectorActionSpaceType,
BrainName = name,
IsTraining = isTraining
};
brainParametersProto.VectorActionDescriptions.AddRange(bp.vectorActionDescriptions);
return(brainParametersProto);
}
/// Helper function that resets all the data structures associated with
/// the agent. Typically used when the agent is being initialized or reset
/// at the end of an episode.
void ResetData()
{
if (brain == null)
{
return;
}
BrainParameters param = brain.brainParameters;
if (param.vectorActionSpaceType == SpaceType.continuous)
{
action.vectorActions = new float[param.vectorActionSize];
info.storedVectorActions = new float[param.vectorActionSize];
}
else
{
action.vectorActions = new float[1];
info.storedVectorActions = new float[1];
}
if (info.textObservation == null)
{
info.textObservation = "";
}
action.textActions = "";
info.memories = new List <float>();
action.memories = new List <float>();
if (param.vectorObservationSpaceType == SpaceType.continuous)
{
info.vectorObservation =
new List <float>(param.vectorObservationSize);
info.stackedVectorObservation =
new List <float>(param.vectorObservationSize
* brain.brainParameters.numStackedVectorObservations);
info.stackedVectorObservation.AddRange(
new float[param.vectorObservationSize
* param.numStackedVectorObservations]);
}
else
{
info.vectorObservation = new List <float>(1);
info.stackedVectorObservation =
new List <float>(param.numStackedVectorObservations);
info.stackedVectorObservation.AddRange(
new float[param.numStackedVectorObservations]);
}
info.visualObservations = new List <Texture2D>();
}
/// Helper function that resets all the data structures associated with
/// the agent. Typically used when the agent is being initialized or reset
/// at the end of an episode.
void ResetData()
{
if (brain == null)
{
return;
}
BrainParameters param = brain.brainParameters;
actionMasker = new ActionMasker(param);
if (param.vectorActionSpaceType == SpaceType.continuous)
{
action.vectorActions = new float[param.vectorActionSize[0]];
info.storedVectorActions = new float[param.vectorActionSize[0]];
}
else
{
action.vectorActions = new float[param.vectorActionSize.Length];
info.storedVectorActions = new float[param.vectorActionSize.Length];
}
if (info.textObservation == null)
{
info.textObservation = "";
}
action.textActions = "";
info.memories = new List <float>();
action.memories = new List <float>();
info.vectorObservation =
new List <float>(param.vectorObservationSize);
info.stackedVectorObservation =
new List <float>(param.vectorObservationSize
* brain.brainParameters.numStackedVectorObservations);
info.stackedVectorObservation.AddRange(
new float[param.vectorObservationSize
* param.numStackedVectorObservations]);
info.visualObservations = new List <Texture2D>();
/// Added by M.Baske to enable frame-stacking.
///
visualObservations = brain.gameObject.GetComponent <VisualObservations>();
if (visualObservations)
{
visualObservations.OnAgentResetData(this);
}
}
/// Helper function that resets all the data structures associated with
/// the agent. Typically used when the agent is being initialized or reset
/// at the end of an episode.
void ResetData()
{
if (brain == null)
{
return;
}
BrainParameters param = brain.brainParameters;
actionMasker = new ActionMasker(param);
// If we haven't initialized vectorActions, initialize to 0. This should only
// happen during the creation of the Agent. In subsequent episodes, vectorAction
// should stay the previous action before the Done(), so that it is properly recorded.
if (action.vectorActions == null)
{
if (param.vectorActionSpaceType == SpaceType.continuous)
{
action.vectorActions = new float[param.vectorActionSize[0]];
info.storedVectorActions = new float[param.vectorActionSize[0]];
}
else
{
action.vectorActions = new float[param.vectorActionSize.Length];
info.storedVectorActions = new float[param.vectorActionSize.Length];
}
}
if (info.textObservation == null)
{
info.textObservation = "";
}
action.textActions = "";
info.memories = new List <float>();
action.memories = new List <float>();
info.vectorObservation =
new List <float>(param.vectorObservationSize);
info.stackedVectorObservation =
new List <float>(param.vectorObservationSize
* brain.brainParameters.numStackedVectorObservations);
info.stackedVectorObservation.AddRange(
new float[param.vectorObservationSize
* param.numStackedVectorObservations]);
info.visualObservations = new List <Texture2D>();
info.customObservation = null;
}
/// <summary>
/// Writes the initial data to the stream.
/// </summary>
public void Initialize(
string demonstrationName, BrainParameters brainParameters, string brainName)
{
if (m_Writer == null)
{
// Already closed
return;
}
m_MetaData = new DemonstrationMetaData {
demonstrationName = demonstrationName
};
var metaProto = m_MetaData.ToProto();
metaProto.WriteDelimitedTo(m_Writer);
WriteBrainParameters(brainName, brainParameters);
}
public override void OnImportAsset(AssetImportContext ctx)
{
var inputType = Path.GetExtension(ctx.assetPath);
if (inputType == null)
{
throw new Exception("Demonstration import error.");
}
try
{
// Read first two proto objects containing metadata and brain parameters.
Stream reader = File.OpenRead(ctx.assetPath);
var metaDataProto = DemonstrationMetaProto.Parser.ParseDelimitedFrom(reader);
var metaData = new DemonstrationMetaData(metaDataProto);
reader.Seek(DemonstrationStore.MetaDataBytes + 1, 0);
var brainParamsProto = BrainParametersProto.Parser.ParseDelimitedFrom(reader);
var brainParameters = new BrainParameters(brainParamsProto);
reader.Close();
var demonstration = ScriptableObject.CreateInstance <Demonstration>();
demonstration.Initialize(brainParameters, metaData);
userData = demonstration.ToString();
Texture2D texture = (Texture2D)
AssetDatabase.LoadAssetAtPath(IconPath, typeof(Texture2D));
#if UNITY_2017_3_OR_NEWER
ctx.AddObjectToAsset(ctx.assetPath, demonstration, texture);
ctx.SetMainObject(demonstration);
#else
ctx.SetMainAsset(ctx.assetPath, demonstration);
#endif
}
catch
{
return;
}
}
internal DiscreteActionMasker(BrainParameters brainParameters)
{
m_BrainParameters = brainParameters;
}
public ActionMasker(BrainParameters brainParameters)
{
m_BrainParameters = brainParameters;
}
public override void OnInspectorGUI()
{
Brain myBrain = (Brain)target;
SerializedObject serializedBrain = serializedObject;
if (myBrain.transform.parent == null)
{
EditorGUILayout.HelpBox(
"A Brain GameObject must be a child of an Academy GameObject!",
MessageType.Error);
}
else if (myBrain.transform.parent.GetComponent <Academy>() == null)
{
EditorGUILayout.HelpBox(
"The Parent of a Brain must have an Academy Component attached to it!",
MessageType.Error);
}
BrainParameters parameters = myBrain.brainParameters;
if (parameters.vectorActionDescriptions == null ||
parameters.vectorActionDescriptions.Length != parameters.vectorActionSize)
{
parameters.vectorActionDescriptions = new string[parameters.vectorActionSize];
}
serializedBrain.Update();
_Foldout = EditorGUILayout.Foldout(_Foldout, "Brain Parameters");
int indentLevel = EditorGUI.indentLevel;
if (_Foldout)
{
EditorGUI.indentLevel++;
EditorGUILayout.LabelField("Vector Observation");
EditorGUI.indentLevel++;
SerializedProperty bpVectorObsSize =
serializedBrain.FindProperty("brainParameters.vectorObservationSize");
EditorGUILayout.PropertyField(bpVectorObsSize, new GUIContent("Space Size",
"Length of state " +
"vector for brain (In Continuous state space)." +
"Or number of possible values (in Discrete state space)."));
SerializedProperty bpNumStackedVectorObs =
serializedBrain.FindProperty("brainParameters.numStackedVectorObservations");
EditorGUILayout.PropertyField(bpNumStackedVectorObs, new GUIContent(
"Stacked Vectors", "Number of states that" +
" will be stacked before beeing fed to the neural network."));
EditorGUI.indentLevel--;
SerializedProperty bpCamResol =
serializedBrain.FindProperty("brainParameters.cameraResolutions");
EditorGUILayout.PropertyField(bpCamResol, new GUIContent("Visual Observation",
"Describes height, " +
"width, and whether to greyscale visual observations for the Brain."), true);
EditorGUILayout.LabelField("Vector Action");
EditorGUI.indentLevel++;
SerializedProperty bpVectorActionType =
serializedBrain.FindProperty("brainParameters.vectorActionSpaceType");
EditorGUILayout.PropertyField(bpVectorActionType, new GUIContent("Space Type",
"Corresponds to whether state" +
" vector contains a single integer (Discrete) " +
"or a series of real-valued floats (Continuous)."));
SerializedProperty bpVectorActionSize =
serializedBrain.FindProperty("brainParameters.vectorActionSize");
EditorGUILayout.PropertyField(bpVectorActionSize, new GUIContent("Space Size",
"Length of action vector " +
"for brain (In Continuous state space)." +
"Or number of possible values (In Discrete action space)."));
SerializedProperty bpVectorActionDescription =
serializedBrain.FindProperty("brainParameters.vectorActionDescriptions");
EditorGUILayout.PropertyField(bpVectorActionDescription, new GUIContent(
"Action Descriptions", "A list of strings used to name" +
" the available actions for the Brain."), true);
}
EditorGUI.indentLevel = indentLevel;
SerializedProperty bt = serializedBrain.FindProperty("brainType");
EditorGUILayout.PropertyField(bt);
if (bt.enumValueIndex < 0)
{
bt.enumValueIndex = (int)BrainType.Player;
}
serializedBrain.ApplyModifiedProperties();
myBrain.UpdateCoreBrains();
myBrain.coreBrain.OnInspector();
#if !NET_4_6 && ENABLE_TENSORFLOW
EditorGUILayout.HelpBox("You cannot have ENABLE_TENSORFLOW without NET_4_6", MessageType.Error);
#endif
}
public void Initialize(BrainParameters brainParameters,
DemonstrationMetaData demonstrationMetaData)
{
this.brainParameters = brainParameters;
metaData = demonstrationMetaData;
}
/// <summary>
/// Sends the Agent info to the linked Brain.
/// </summary>
void SendInfoToBrain()
{
if (brain == null)
{
return;
}
info.memories = action.memories;
info.storedVectorActions = action.vectorActions;
info.storedTextActions = action.textActions;
info.vectorObservation.Clear();
actionMasker.ResetMask();
CollectObservations();
info.actionMasks = actionMasker.GetMask();
BrainParameters param = brain.brainParameters;
if (info.vectorObservation.Count != param.vectorObservationSize)
{
throw new UnityAgentsException(string.Format(
"Vector Observation size mismatch between continuous " +
"agent {0} and brain {1}. " +
"Was Expecting {2} but received {3}. ",
gameObject.name, brain.name,
brain.brainParameters.vectorObservationSize,
info.vectorObservation.Count));
}
info.stackedVectorObservation.RemoveRange(
0, param.vectorObservationSize);
info.stackedVectorObservation.AddRange(info.vectorObservation);
info.visualObservations.Clear();
var visualObservationCount = agentParameters.agentCameras.Count + agentParameters.agentRenderTextures.Count;
if (param.cameraResolutions.Length > visualObservationCount)
{
throw new UnityAgentsException(string.Format(
"Not enough cameras/renderTextures for agent {0} : Brain {1} expecting at " +
"least {2} cameras/renderTextures but only {3} were present.",
gameObject.name, brain.name,
brain.brainParameters.cameraResolutions.Length,
visualObservationCount));
}
//First add all cameras
for (int i = 0; i < agentParameters.agentCameras.Count; i++)
{
ObservationToTexture(
agentParameters.agentCameras[i],
param.cameraResolutions[i].width,
param.cameraResolutions[i].height,
ref textureArray[i]);
info.visualObservations.Add(textureArray[i]);
}
//Then add all renderTextures
var camCount = agentParameters.agentCameras.Count;
for (int i = 0; i < agentParameters.agentRenderTextures.Count; i++)
{
ObservationToTexture(
agentParameters.agentRenderTextures[i],
param.cameraResolutions[camCount + i].width,
param.cameraResolutions[camCount + i].height,
ref textureArray[i]);
info.visualObservations.Add(textureArray[i]);
}
info.reward = reward;
info.done = done;
info.maxStepReached = maxStepReached;
info.id = id;
brain.SendState(this, info);
if (recorder != null && recorder.record && Application.isEditor)
{
recorder.WriteExperience(info);
}
info.textObservation = "";
}
public ActionMasker(BrainParameters brainParameters)
{
this._brainParameters = brainParameters;
}
/// <summary>
/// Sends the Agent info to the linked Brain.
/// </summary>
void SendInfoToBrain()
{
if (brain == null)
{
return;
}
info.memories = action.memories;
info.storedVectorActions = action.vectorActions;
info.storedTextActions = action.textActions;
info.vectorObservation.Clear();
CollectObservations();
BrainParameters param = brain.brainParameters;
if (param.vectorObservationSpaceType == SpaceType.continuous)
{
if (info.vectorObservation.Count != param.vectorObservationSize)
{
throw new UnityAgentsException(string.Format(
"Vector Observation size mismatch between continuous " +
"agent {0} and brain {1}. " +
"Was Expecting {2} but received {3}. ",
gameObject.name, brain.gameObject.name,
brain.brainParameters.vectorObservationSize,
info.vectorObservation.Count));
}
info.stackedVectorObservation.RemoveRange(
0, param.vectorObservationSize);
info.stackedVectorObservation.AddRange(info.vectorObservation);
}
else
{
if (info.vectorObservation.Count != 1)
{
throw new UnityAgentsException(string.Format(
"Vector Observation size mismatch between discrete agent" +
" {0} and brain {1}. Was Expecting {2} but received {3}. ",
gameObject.name, brain.gameObject.name,
1, info.vectorObservation.Count));
}
info.stackedVectorObservation.RemoveRange(0, 1);
info.stackedVectorObservation.AddRange(info.vectorObservation);
}
info.visualObservations.Clear();
if (param.cameraResolutions.Length > agentParameters.agentCameras.Count)
{
throw new UnityAgentsException(string.Format(
"Not enough cameras for agent {0} : Bain {1} expecting at " +
"least {2} cameras but only {3} were present.",
gameObject.name, brain.gameObject.name,
brain.brainParameters.cameraResolutions.Length,
agentParameters.agentCameras.Count));
}
for (int i = 0; i < brain.brainParameters.cameraResolutions.Length; i++)
{
info.visualObservations.Add(ObservationToTexture(
agentParameters.agentCameras[i],
param.cameraResolutions[i].width,
param.cameraResolutions[i].height));
}
info.reward = reward;
info.done = done;
info.maxStepReached = maxStepReached;
info.id = id;
brain.SendState(this, info);
info.textObservation = "";
}
/// <summary>
/// Sends the Agent info to the linked Brain.
/// </summary>
void SendInfoToBrain()
{
if (brain == null)
{
return;
}
info.memories = action.memories;
info.storedVectorActions = action.vectorActions;
info.storedTextActions = action.textActions;
info.vectorObservation.Clear();
actionMasker.ResetMask();
CollectObservations();
info.actionMasks = actionMasker.GetMask();
BrainParameters param = brain.brainParameters;
if (info.vectorObservation.Count != param.vectorObservationSize)
{
throw new UnityAgentsException(string.Format(
"Vector Observation size mismatch between continuous " +
"agent {0} and brain {1}. " +
"Was Expecting {2} but received {3}. ",
gameObject.name, brain.gameObject.name,
brain.brainParameters.vectorObservationSize,
info.vectorObservation.Count));
}
info.stackedVectorObservation.RemoveRange(
0, param.vectorObservationSize);
info.stackedVectorObservation.AddRange(info.vectorObservation);
info.visualObservations.Clear();
/// Modified by M.Baske to enable frame-stacking.
///
if (visualObservations)
{
visualObservations.ApplyObservations(this, info.visualObservations);
}
/// Original code:
/*
* if (param.cameraResolutions.Length > agentParameters.agentCameras.Count)
* {
* throw new UnityAgentsException(string.Format(
* "Not enough cameras for agent {0} : Bain {1} expecting at " +
* "least {2} cameras but only {3} were present.",
* gameObject.name, brain.gameObject.name,
* brain.brainParameters.cameraResolutions.Length,
* agentParameters.agentCameras.Count));
* }
*
* for (int i = 0; i < brain.brainParameters.cameraResolutions.Length; i++)
* {
* ObservationToTexture(
* agentParameters.agentCameras[i],
* param.cameraResolutions[i].width,
* param.cameraResolutions[i].height,
* ref textureArray[i]);
* info.visualObservations.Add(textureArray[i]);
* }
*/
info.reward = reward;
info.done = done;
info.maxStepReached = maxStepReached;
info.id = id;
brain.SendState(this, info);
info.textObservation = "";
}
public void Awake()
{
brainParameters =
gameObject.GetComponent <Brain>().brainParameters;
actionSpaceType = brainParameters.vectorActionSpaceType;
}