public virtual ReinforcementLearningAction Step(ReinforcementLearningEnvironment env, ReinforcementLearningPolicy policy)
{
ReinforcementLearningState state = env.State();
float reward = env.Reward();
ReinforcementLearningAction action = policy.ActionForState(state, qFunction);
if ((prevState != null) && (prevAction != null))
{
ReinforcementLearningAction bestAction;
float Qtp1max;
qFunction.GetBestActionAndUtilityForState(state, out bestAction, out Qtp1max);
float Qt = qFunction.Evaluate(prevState, prevAction);
float deltaQ = Alpha * (reward + Discount * Qtp1max - Qt);
if (float.IsNaN(deltaQ)) throw new Exception();
qFunction.ModifyValue(prevState, prevAction, deltaQ);
}
prevAction = action;
prevState = state;
return action;
}
public override ReinforcementLearningAction Step(ReinforcementLearningEnvironment env, ReinforcementLearningPolicy policy)
{
ReinforcementLearningState state = env.State();
float reward = env.Reward();
ReinforcementLearningAction action = policy.ActionForState(state, qFunction);
if ((prevState != null) && (prevAction != null))
{
ReinforcementLearningAction bestAction;
float Qtp1max;
qFunction.GetBestActionAndUtilityForState(state, out bestAction, out Qtp1max);
float Qt = qFunction.Evaluate(prevState, prevAction);
float newQ = Alpha * (reward + Discount * Qtp1max - Qt) + Qt;
InvertedPendulumESIGMNQStore qs = ((InvertedPendulumESIGMNQStore)qFunction);
qs.ReplaceValue((InvertedPendulumState)prevState, (InvertedPendulumAction2)prevAction, newQ);
}
prevAction = action;
prevState = state;
return action;
}
