/// Uses the communicator to retrieve the actions, memories and values and
/// sends them to the agents
public void DecideAction(Dictionary <Agent, AgentInfo> agentInfo)
{
if (brainBatcher != null)
{
brainBatcher.SendBrainInfo(brain.gameObject.name, agentInfo);
}
}
/// Uses the Decision Component to decide that action to take
public void DecideAction(Dictionary <Agent, AgentInfo> agentInfo)
{
if (brainBatcher != null)
{
brainBatcher.SendBrainInfo(brain.gameObject.name, agentInfo);
}
if (decision == null)
{
throw new UnityAgentsException(
"The Brain is set to Heuristic, but no decision script attached to it");
}
foreach (var agent in agentInfo.Keys)
{
agent.UpdateVectorAction(decision.Decide(
agentInfo[agent].stackedVectorObservation,
agentInfo[agent].visualObservations,
agentInfo[agent].reward,
agentInfo[agent].done,
agentInfo[agent].memories));
}
foreach (var agent in agentInfo.Keys)
{
agent.UpdateMemoriesAction(decision.MakeMemory(
agentInfo[agent].stackedVectorObservation,
agentInfo[agent].visualObservations,
agentInfo[agent].reward,
agentInfo[agent].done,
agentInfo[agent].memories));
}
}
/// Uses the continuous inputs or dicrete inputs of the player to
/// decide action
public void DecideAction(Dictionary <Agent, AgentInfo> agentInfo)
{
if (brainBatcher != null)
{
brainBatcher.SendBrainInfo(brain.gameObject.name, agentInfo);
}
if (brain.brainParameters.vectorActionSpaceType == SpaceType.continuous)
{
foreach (var agent in agentInfo.Keys)
{
var action = new float[brain.brainParameters.vectorActionSize];
foreach (var cha in keyContinuousPlayerActions)
{
if (Input.GetKey(cha.key))
{
action[cha.index] = cha.value;
}
}
foreach (var axisAction in axisContinuousPlayerActions)
{
var axisValue = Input.GetAxis(axisAction.axis);
axisValue *= axisAction.scale;
if (Mathf.Abs(axisValue) > 0.0001)
{
action[axisAction.index] = axisValue;
}
}
agent.UpdateVectorAction(action);
}
}
else
{
foreach (var agent in agentInfo.Keys)
{
var action = new float[1] {
defaultAction
};
foreach (var dha in discretePlayerActions)
{
if (Input.GetKey(dha.key))
{
action[0] = dha.value;
break;
}
}
agent.UpdateVectorAction(action);
}
}
}
/// Uses the stored information to run the tensorflow graph and generate
/// the actions.
public void DecideAction(Dictionary <Agent, AgentInfo> agentInfo)
{
#if ENABLE_TENSORFLOW
if (brainBatcher != null)
{
brainBatcher.SendBrainInfo(brain.gameObject.name, agentInfo);
}
int currentBatchSize = agentInfo.Count();
List <Agent> agentList = agentInfo.Keys.ToList();
if (currentBatchSize == 0)
{
return;
}
// Create the state tensor
if (hasState)
{
int stateLength = 1;
stateLength = brain.brainParameters.vectorObservationSize;
inputState =
new float[currentBatchSize, stateLength *brain.brainParameters.numStackedVectorObservations];
var i = 0;
foreach (Agent agent in agentList)
{
List <float> stateList = agentInfo[agent].stackedVectorObservation;
for (int j =
0;
j < stateLength * brain.brainParameters.numStackedVectorObservations;
j++)
{
inputState[i, j] = stateList[j];
}
i++;
}
}
// Create the state tensor
if (hasPrevAction)
{
int totalNumberActions = brain.brainParameters.vectorActionSize.Length;
inputPrevAction = new int[currentBatchSize, totalNumberActions];
var i = 0;
foreach (Agent agent in agentList)
{
float[] actionList = agentInfo[agent].storedVectorActions;
for (var j = 0; j < totalNumberActions; j++)
{
inputPrevAction[i, j] = Mathf.FloorToInt(actionList[j]);
}
i++;
}
}
if (hasMaskedActions)
{
maskedActions = new float[
currentBatchSize,
brain.brainParameters.vectorActionSize.Sum()
];
var i = 0;
foreach (Agent agent in agentList)
{
for (int j = 0; j < brain.brainParameters.vectorActionSize.Sum(); j++)
{
if (agentInfo[agent].actionMasks != null)
{
maskedActions[i, j] = agentInfo[agent].actionMasks[j] ? 0.0f : 1.0f;
}
else
{
maskedActions[i, j] = 1.0f;
}
}
i++;
}
}
observationMatrixList.Clear();
for (int observationIndex =
0;
observationIndex < brain.brainParameters.cameraResolutions.Length;
observationIndex++)
{
texturesHolder.Clear();
foreach (Agent agent in agentList)
{
texturesHolder.Add(agentInfo[agent].visualObservations[observationIndex]);
}
observationMatrixList.Add(
BatchVisualObservations(texturesHolder,
brain.brainParameters.cameraResolutions[observationIndex].blackAndWhite));
}
// Create the recurrent tensor
if (hasRecurrent)
{
// Need to have variable memory size
inputOldMemories = new float[currentBatchSize, memorySize];
var i = 0;
foreach (Agent agent in agentList)
{
float[] m = agentInfo[agent].memories.ToArray();
for (int j = 0; j < m.Length; j++)
{
inputOldMemories[i, j] = m[j];
}
i++;
}
}
var runner = session.GetRunner();
try
{
runner.Fetch(graph[graphScope + ActionPlaceholderName][0]);
}
catch
{
throw new UnityAgentsException(string.Format(
@"The node {0} could not be found. Please make sure the graphScope {1} is correct",
graphScope + ActionPlaceholderName, graphScope));
}
if (hasBatchSize)
{
runner.AddInput(graph[graphScope + BatchSizePlaceholderName][0], new int[] { currentBatchSize });
}
foreach (TensorFlowAgentPlaceholder placeholder in graphPlaceholders)
{
try
{
if (placeholder.valueType == TensorFlowAgentPlaceholder.TensorType.FloatingPoint)
{
runner.AddInput(graph[graphScope + placeholder.name][0],
new float[] { Random.Range(placeholder.minValue, placeholder.maxValue) });
}
else if (placeholder.valueType == TensorFlowAgentPlaceholder.TensorType.Integer)
{
runner.AddInput(graph[graphScope + placeholder.name][0],
new int[] { Random.Range((int)placeholder.minValue, (int)placeholder.maxValue + 1) });
}
}
catch
{
throw new UnityAgentsException(string.Format(
@"One of the Tensorflow placeholder cound nout be found.
In brain {0}, there are no {1} placeholder named {2}.",
brain.gameObject.name, placeholder.valueType.ToString(), graphScope + placeholder.name));
}
}
// Create the state tensor
if (hasState)
{
runner.AddInput(graph[graphScope + VectorObservationPlacholderName][0], inputState);
}
// Create the previous action tensor
if (hasPrevAction)
{
runner.AddInput(graph[graphScope + PreviousActionPlaceholderName][0], inputPrevAction);
}
// Create the mask action tensor
if (hasMaskedActions)
{
runner.AddInput(graph[graphScope + ActionMaskPlaceholderName][0], maskedActions);
}
// Create the observation tensors
for (int obsNumber =
0;
obsNumber < brain.brainParameters.cameraResolutions.Length;
obsNumber++)
{
runner.AddInput(graph[graphScope + VisualObservationPlaceholderName[obsNumber]][0],
observationMatrixList[obsNumber]);
}
if (hasRecurrent)
{
runner.AddInput(graph[graphScope + "sequence_length"][0], 1);
runner.AddInput(graph[graphScope + RecurrentInPlaceholderName][0], inputOldMemories);
runner.Fetch(graph[graphScope + RecurrentOutPlaceholderName][0]);
}
if (hasValueEstimate)
{
runner.Fetch(graph[graphScope + "value_estimate"][0]);
}
TFTensor[] networkOutput;
try
{
networkOutput = runner.Run();
}
catch (TFException e)
{
string errorMessage = e.Message;
try
{
errorMessage =
$@"The tensorflow graph needs an input for {e.Message.Split(new string[] {"Node: "}, 0)[1].Split('=')[0]} of type {e.Message.Split(new string[] {"dtype="}, 0)[1].Split(',')[0]}";
}
finally
{
throw new UnityAgentsException(errorMessage);
}
}
// Create the recurrent tensor
if (hasRecurrent)
{
float[,] recurrentTensor = networkOutput[1].GetValue() as float[, ];
var i = 0;
foreach (Agent agent in agentList)
{
var m = new float[memorySize];
for (int j = 0; j < memorySize; j++)
{
m[j] = recurrentTensor[i, j];
}
agent.UpdateMemoriesAction(m.ToList());
i++;
}
}
if (hasValueEstimate)
{
float[,] value_estimates = new float[currentBatchSize, 1];
if (hasRecurrent)
{
value_estimates = networkOutput[2].GetValue() as float[, ];
}
else
{
value_estimates = networkOutput[1].GetValue() as float[, ];
}
var i = 0;
foreach (Agent agent in agentList)
{
agent.UpdateValueAction(value_estimates[i, 0]);
}
}
if (brain.brainParameters.vectorActionSpaceType == SpaceType.continuous)
{
var output = networkOutput[0].GetValue() as float[, ];
var i = 0;
foreach (Agent agent in agentList)
{
var a = new float[brain.brainParameters.vectorActionSize[0]];
for (int j = 0; j < brain.brainParameters.vectorActionSize[0]; j++)
{
a[j] = output[i, j];
}
agent.UpdateVectorAction(a);
i++;
}
}
else if (brain.brainParameters.vectorActionSpaceType == SpaceType.discrete)
{
long[,] output = networkOutput[0].GetValue() as long[, ];
var i = 0;
foreach (Agent agent in agentList)
{
var actSize = brain.brainParameters.vectorActionSize.Length;
var a = new float[actSize];
for (int actIdx = 0; actIdx < actSize; actIdx++)
{
a[actIdx] = output[i, actIdx];
}
agent.UpdateVectorAction(a);
i++;
}
}
#else
if (agentInfo.Count > 0)
{
throw new UnityAgentsException(string.Format(
@"The brain {0} was set to Internal but the Tensorflow
library is not present in the Unity project.",
brain.gameObject.name));
}
#endif
}
/// <summary>
/// Calls the DecideAction method that the concrete brain implements.
/// </summary>
private void BrainDecideAction()
{
m_BrainBatcher?.SendBrainInfo(name, m_AgentInfos);
DecideAction();
}
