/// Uses the continuous inputs or dicrete inputs of the player to
/// decide action
public void DecideAction()
{
if (ctrl == null)
{
ctrl = brain.brainParameters.syft.controller;
}
if (policy == null)
{
found_policy = false;
}
else
{
found_policy = true;
}
if (ctrl.getAgent(1234) != null)
{
policy = ctrl.getAgent(1234);
if (found_policy == false)
{
foreach (KeyValuePair <int, global::Agent> idAgent in brain.agents)
{
idAgent.Value.Reset();
}
}
}
else
{
policy = null;
}
//The states are collected in order to debug the CollectStates method.
Dictionary <int, List <float> > states = brain.CollectStates();
Dictionary <int, float> rewards = brain.CollectRewards();
Dictionary <int, bool> dones = brain.CollectDones();
if (brain.brainParameters.actionSpaceType == StateType.continuous)
{
float[] action = new float[brain.brainParameters.actionSize];
foreach (ContinuousPlayerAction cha in continuousPlayerActions)
{
if (Input.GetKey(cha.key))
{
action[cha.index] = cha.value;
}
}
Dictionary <int, float[]> actions = new Dictionary <int, float[]>();
foreach (KeyValuePair <int, global::Agent> idAgent in brain.agents)
{
actions.Add(idAgent.Key, action);
}
brain.SendActions(actions);
}
else
{
float[] action = new float[1] {
defaultAction
};
foreach (DiscretePlayerAction dha in discretePlayerActions)
{
if (Input.GetKey(dha.key))
{
action[0] = (float)dha.value;
break;
}
}
Dictionary <int, float[]> actions = new Dictionary <int, float[]>();
foreach (KeyValuePair <int, global::Agent> idAgent in brain.agents)
{
if (policy == null)
{
// do nothing - you don't have a network
actions.Add(idAgent.Key, new float[1] {
0
});
}
else
{
//input = [Number of agents x state size]
FloatTensor input = ctrl.floatTensorFactory.Create(_shape: new int[] { 1, states[idAgent.Key].Count },
_data: states[idAgent.Key].ToArray());
IntTensor pred = policy.Sample(input);
actions.Add(idAgent.Key, new float[1] {
pred.Data[0]
});
}
}
brain.SendActions(actions);
}
}
