/// Collects information from the agents and store them
public void SendState()
{
#if ENABLE_TENSORFLOW
agentKeys = new List <int>(brain.agents.Keys);
currentBatchSize = brain.agents.Count;
if (currentBatchSize == 0)
{
if (coord != null)
{
coord.giveBrainInfo(brain);
}
return;
}
// Create the state tensor
if (hasState)
{
Dictionary <int, List <float> > states = brain.CollectStates();
inputState = new float[currentBatchSize, brain.brainParameters.stateSize];
int i = 0;
foreach (int k in agentKeys)
{
List <float> state_list = states[k];
for (int j = 0; j < brain.brainParameters.stateSize; j++)
{
inputState[i, j] = state_list[j];
}
i++;
}
}
// Create the observation tensors
observationMatrixList = brain.GetObservationMatrixList(agentKeys);
// Create the recurrent tensor
if (hasRecurrent)
{
Dictionary <int, float[]> old_memories = brain.CollectMemories();
inputOldMemories = new float[currentBatchSize, brain.brainParameters.memorySize];
int i = 0;
foreach (int k in agentKeys)
{
float[] m = old_memories[k];
for (int j = 0; j < brain.brainParameters.memorySize; j++)
{
inputOldMemories[i, j] = m[j];
}
i++;
}
}
if (coord != null)
{
coord.giveBrainInfo(brain);
}
#endif
}
/// Uses the communicator to send the states, observations, rewards and
/// dones outside of Unity
public void SendState()
{
if (coord != null)
{
coord.giveBrainInfo(brain);
}
}
/// Nothing to implement, the Player does not use the state to make
/// decisions
public void SendState()
{
if (coord != null)
{
coord.giveBrainInfo(brain);
}
else
{
//The states are collected in order to debug the CollectStates method.
brain.CollectStates();
}
}
public void SendState()
{
Dictionary <int, List <float> > states = brain.CollectStates();
Dictionary <int, float> rewards = brain.CollectRewards();
Dictionary <int, bool> dones = brain.CollectDones();
if (ctrl == null)
{
ctrl = brain.brainParameters.syft.controller;
}
if (coord != null)
{
coord.giveBrainInfo(brain);
}
else
{
if (policy != null)
{
foreach (KeyValuePair <int, List <float> > idAgent in states)
{
float raw_reward = rewards[idAgent.Key];
FloatTensor reward = ctrl.floatTensorFactory.Create(_shape: new int[] { 1 },
_data: new float[] { raw_reward });
policy.HookReward(reward);
if (dones[idAgent.Key])
{
policy.Learn();
}
}
}
}
}
/// Collects information from the agents and store them
public void SendState()
{
#if ENABLE_TENSORFLOW
agentKeys = new List <int>(brain.agents.Keys);
currentBatchSize = brain.agents.Count;
if (currentBatchSize == 0)
{
if (coord != null)
{
coord.giveBrainInfo(brain);
}
return;
}
// Create the state tensor
if (hasState)
{
Dictionary <int, List <float> > states = brain.CollectStates();
inputState = new float[currentBatchSize, brain.brainParameters.stateSize];
int i = 0;
foreach (int k in agentKeys)
{
List <float> state_list = states[k];
for (int j = 0; j < brain.brainParameters.stateSize; j++)
{
inputState[i, j] = state_list[j];
}
i++;
}
}
// Create the observation tensors
observationMatrixList = brain.GetObservationMatrixList(agentKeys);
float[,,,] data = new float[agentKeys.Count, 20, 20, 7];
for (int i = 0; i < agentKeys.Count; i++)
{
for (int j = 0; j < 20; j++)
{
for (int k = 0; k < 20; k++)
{
for (int c = 0; c < 7; c++)
{
data[i, j, k, c] = inputState[i, j * 140 + k * 7 + c];
}
}
}
}
var tmp = inputState;
inputState = new float[currentBatchSize, 8];
for (int i = 0; i < agentKeys.Count; i++)
{
for (int j = 0; j < 8; j++)
{
inputState[i, j] = tmp[i, 2800 + j];
}
}
observationMatrixList.Add(data);
// Create the recurrent tensor
if (hasRecurrent)
{
Dictionary <int, float[]> old_memories = brain.CollectMemories();
inputOldMemories = new float[currentBatchSize, brain.brainParameters.memorySize];
int i = 0;
foreach (int k in agentKeys)
{
float[] m = old_memories[k];
for (int j = 0; j < brain.brainParameters.memorySize; j++)
{
inputOldMemories[i, j] = m[j];
}
i++;
}
}
if (coord != null)
{
coord.giveBrainInfo(brain);
}
#endif
}
/// Uses the communicator to send the states, observations, rewards and
/// dones outside of Unity
public void SendState()
{
coord.giveBrainInfo(brain);
}
