/**
* Passive reinforcement learning based on adaptive dynamic programming.
*
* @param percept
* a percept indicating the current state s' and reward signal
* r'.
* @return an action
*/
public override A execute(IPerceptStateReward <S> percept)
{
// if s' is new then U[s'] <- r'; R[s'] <- r'
S sDelta = percept.state();
double rDelta = percept.reward();
if (!U.ContainsKey(sDelta))
{
U.Put(sDelta, rDelta);
R.Put(sDelta, rDelta);
}
// if s is not null then
if (null != s)
{
// increment N<sub>sa</sub>[s,a] and N<sub>s'|sa</sub>[s',s,a]
Pair <S, A> sa = new Pair <S, A>(s, a);
Nsa.incrementFor(sa);
NsDelta_sa.incrementFor(new Pair <S, Pair <S, A> >(sDelta, sa));
// for each t such that N<sub>s'|sa</sub>[t,s,a] is nonzero do
foreach (S t in mdp.states())
{
Pair <S, Pair <S, A> > t_sa = new Pair <S, Pair <S, A> >(t, sa);
if (0 != NsDelta_sa.getCount(t_sa))
{
// P(t|s,a) <- N<sub>s'|sa</sub>[t,s,a] /
// N<sub>sa</sub>[s,a]
P.Put(t_sa, (double)NsDelta_sa.getCount(t_sa)
/ (double)Nsa.getCount(sa));
}
}
}
// U <- POLICY-EVALUATION(π, U, mdp)
U = policyEvaluation.evaluate(pi, U, mdp);
// if s'.TERMINAL? then s,a <- null else s,a <- s',π[s']
if (isTerminal(sDelta))
{
s = default(S);
a = default(A);
}
else
{
s = sDelta;
a = pi.Get(sDelta);
}
// return a
return(a);
}
