public override Dictionary <Agent, TakeActionOutput> TakeAction(Dictionary <Agent, AgentInfoInternal> agentInfos)
{
var result = new Dictionary <Agent, TakeActionOutput>();
var agentList = new List <Agent>(agentInfos.Keys);
float[,] vectorObsAll = CreateVectorInputBatch(agentInfos, agentList);
var visualObsAll = CreateVisualInputBatch(agentInfos, agentList, BrainToTrain.brainParameters.cameraResolutions);
var actionMasks = CreateActionMasks(agentInfos, agentList, BrainToTrain.brainParameters.vectorActionSize);
float[,] actions = null;
actions = modeNE.EvaluateActionNE(vectorObsAll, visualObsAll, actionMasks);
int i = 0;
foreach (var agent in agentList)
{
var info = agentInfos[agent];
//use result from neural network directly
var tempAction = new TakeActionOutput();
tempAction.outputAction = actions.GetRow(i);
result[agent] = tempAction;
i++;
}
return(result);
}
public override Dictionary <Agent, TakeActionOutput> TakeAction(Dictionary <Agent, AgentInfoInternal> agentInfos)
{
var result = new Dictionary <Agent, TakeActionOutput>();
var agentList = new List <Agent>(agentInfos.Keys);
if (agentList.Count <= 0)
{
return(result);
}
float[,] vectorObsAll = CreateVectorInputBatch(agentInfos, agentList);
var visualObsAll = CreateVisualInputBatch(agentInfos, agentList, BrainToTrain.brainParameters.cameraResolutions);
var actionMasks = CreateActionMasks(agentInfos, agentList, BrainToTrain.brainParameters.vectorActionSize);
float[,] actionProbs = null;
var values = iModelPPO.EvaluateValue(vectorObsAll, visualObsAll);
var actions = iModelPPO.EvaluateAction(vectorObsAll, out actionProbs, visualObsAll, actionMasks);
int i = 0;
foreach (var agent in agentList)
{
var agentDecision = agent.GetComponent <AgentDependentDecision>();
if (isTraining && agentDecision != null && agentDecision.useDecision && UnityEngine.Random.Range(0, 1.0f) <= parametersPPO.useHeuristicChance)
{
//if this agent will use the decision, use it
var info = agentInfos[agent];
var action = agentDecision.Decide(info.stackedVectorObservation, info.visualObservations, new List <float>(actions.GetRow(i)));
float[,] vectorOb = CreateVectorInputBatch(agentInfos, new List <Agent>()
{
agent
});
var visualOb = CreateVisualInputBatch(agentInfos, new List <Agent>()
{
agent
}, BrainToTrain.brainParameters.cameraResolutions);
var probs = iModelPPO.EvaluateProbability(vectorOb, action.Reshape(1, action.Length), visualOb, actionMasks);
var temp = new TakeActionOutput();
temp.allProbabilities = probs.GetRow(0);
temp.outputAction = action;
temp.value = values[i];
result[agent] = temp;
}
else
{
var temp = new TakeActionOutput();
temp.allProbabilities = actionProbs.GetRow(i);
temp.outputAction = actions.GetRow(i);
temp.value = values[i];
result[agent] = temp;
}
i++;
}
return(result);
}
public Dictionary <Agent, TakeActionOutput> TakeAction(Dictionary <Agent, AgentInfo> agentInfos)
{
var result = new Dictionary <Agent, TakeActionOutput>();
foreach (var a in agentInfos)
{
AgentES agent = a.Key as AgentES;
if (agent != null)
{
if (agent.synchronizedDecision)
{
result[agent] = new TakeActionOutput()
{
outputAction = Array.ConvertAll(agent.Optimize(), t => (float)t)
};
}
else
{
agent.OptimizeAsync();
}
}
}
return(new Dictionary <Agent, TakeActionOutput>());
}
public override Dictionary <Agent, TakeActionOutput> TakeAction(Dictionary <Agent, AgentInfo> agentInfos)
{
var result = new Dictionary <Agent, TakeActionOutput>();
var agentList = new List <Agent>(agentInfos.Keys);
if (agentList.Count <= 0)
{
return(result);
}
float[,] vectorObsAll = CreateVectorInputBatch(agentInfos, agentList);
var visualObsAll = CreateVisualInputBatch(agentInfos, agentList, BrainToTrain.brainParameters.cameraResolutions);
float[,] actions = null;
var evalOutput = modelSL.EvaluateAction(vectorObsAll, visualObsAll);
actions = evalOutput.Item1;
var vars = evalOutput.Item2;
int i = 0;
int agentNumWithDecision = 0;
float actionDiff = 0; //difference between decision and from networknetwork
foreach (var agent in agentList)
{
var info = agentInfos[agent];
var agentDecision = agent.GetComponent <AgentDependentDecision>();
if (agentDecision != null && agentDecision.useDecision)
{
//if this agent will use the decision, use it
float[] action = null;
if (vars != null)
{
action = agentDecision.Decide(info.stackedVectorObservation, info.visualObservations, new List <float>(actions.GetRow(i)), new List <float>(vars.GetRow(i)));
}
else
{
action = agentDecision.Decide(info.stackedVectorObservation, info.visualObservations, new List <float>(actions.GetRow(i)));
}
var tempAction = new TakeActionOutput();
tempAction.outputAction = action;
result[agent] = tempAction;
if (BrainToTrain.brainParameters.vectorActionSpaceType == SpaceType.continuous)
{
actionDiff += Enumerable.Zip(action, actions.GetRow(i), (a, b) => Mathf.Sqrt((a - b) * (a - b))).Aggregate((a, v) => a + v);
}
else
{
actionDiff += Enumerable.Zip(action, actions.GetRow(i), (a, b) => (Mathf.RoundToInt(a) == Mathf.RoundToInt(b))?0:1).Aggregate((a, v) => a + v);
}
agentNumWithDecision++;
}
else
{
//use result from neural network directly
var tempAction = new TakeActionOutput();
tempAction.outputAction = actions.GetRow(i);
result[agent] = tempAction;
}
i++;
}
if (agentNumWithDecision > 0)
{
stats.AddData("action difference", actionDiff / agentNumWithDecision);
}
return(result);
}
public override Dictionary <Agent, TakeActionOutput> TakeAction(Dictionary <Agent, AgentInfoInternal> agentInfos)
{
var result = new Dictionary <Agent, TakeActionOutput>();
var agentList = new List <Agent>(agentInfos.Keys);
if (agentList.Count <= 0)
{
return(result);
}
float[,] vectorObsAll = CreateVectorInputBatch(agentInfos, agentList);
var visualObsAll = CreateVisualInputBatch(agentInfos, agentList, BrainToTrain.brainParameters.cameraResolutions);
var values = iModelPPO.EvaluateValue(vectorObsAll, visualObsAll);
var actions = iModelPPO.EvaluateAction(vectorObsAll, visualObsAll);
/*if (GetStep() < pretrainDataCollectingSteps)
* {
* //collecting data for pretrain
* for (int n = 0; n < vectorObsAll.GetLength(1); ++n)
* {
* for (int m = 0; m < vectorObsAll.GetLength(0); ++m)
* {
* pretrainObservationDataCollect[n].Add(vectorObsAll[m, n]);
* }
* }
* }*/
int i = 0;
foreach (var agent in agentList)
{
var agentDecision = agent.GetComponent <AgentDependentDecision>();
if (isTraining && agentDecision != null && agentDecision.useDecision)// && UnityEngine.Random.Range(0, 1.0f) <= parametersPPO.useHeuristicChance)
{
//if this agent will use the decision, use it
var info = agentInfos[agent];
var action = agentDecision.Decide(info.stackedVectorObservation, info.visualObservations, new List <float>(actions.GetRow(i)));
float[,] vectorOb = CreateVectorInputBatch(agentInfos, new List <Agent>()
{
agent
});
var visualOb = CreateVisualInputBatch(agentInfos, new List <Agent>()
{
agent
}, BrainToTrain.brainParameters.cameraResolutions);
var temp = new TakeActionOutput();
temp.outputAction = action;
temp.value = values[i];
result[agent] = temp;
}
else
{
var temp = new TakeActionOutput();
//temp.allProbabilities = actionProbs.GetRow(i);
temp.outputAction = actions.GetRow(i);
temp.value = values[i];
result[agent] = temp;
}
i++;
}
return(result);
}
