// function VALUE-ITERATION(mdp, ε) returns a utility function
/**
* The value iteration algorithm for calculating the utility of states.
*
* @param mdp
* an MDP with states S, actions A(s), <br>
* transition model P(s' | s, a), rewards R(s)
* @param epsilon
* the maximum error allowed in the utility of any state
* @return a vector of utilities for states in S
*/
public IMap <S, double> valueIteration(IMarkovDecisionProcess <S, A> mdp, double epsilon)
{
//
// local variables: U, U', vectors of utilities for states in S,
// initially zero
IMap <S, double> U = Util.create(mdp.states(), 0D);
IMap <S, double> Udelta = Util.create(mdp.states(), 0D);
// δ the maximum change in the utility of any state in an
// iteration
double delta = 0;
// Note: Just calculate this once for efficiency purposes:
// ε(1 - γ)/γ
double minDelta = epsilon * (1 - gamma) / gamma;
// repeat
do
{
// U <- U'; δ <- 0
U.PutAll(Udelta);
delta = 0;
// for each state s in S do
foreach (S s in mdp.states())
{
// max<sub>a ∈ A(s)</sub>
ISet <A> actions = mdp.actions(s);
// Handle terminal states (i.e. no actions).
double aMax = 0;
if (actions.Size() > 0)
{
aMax = double.NegativeInfinity;
}
foreach (A a in actions)
{
// Σ<sub>s'</sub>P(s' | s, a) U[s']
double aSum = 0;
foreach (S sDelta in mdp.states())
{
aSum += mdp.transitionProbability(sDelta, s, a) * U.Get(sDelta);
}
if (aSum > aMax)
{
aMax = aSum;
}
}
// U'[s] <- R(s) + γ
// max<sub>a ∈ A(s)</sub>
Udelta.Put(s, mdp.reward(s) + gamma * aMax);
// if |U'[s] - U[s]| > δ then δ <- |U'[s] - U[s]|
double aDiff = System.Math.Abs(Udelta.Get(s) - U.Get(s));
if (aDiff > delta)
{
delta = aDiff;
}
}
// until δ < ε(1 - γ)/γ
} while (delta > minDelta);
// return U
return(U);
}
/**
* Create a policy vector indexed by state, initially random.
*
* @param mdp
* an MDP with states S, actions A(s), transition model P(s'|s,a)
* @return a policy vector indexed by state, initially random.
*/
public static IMap <S, A> initialPolicyVector(IMarkovDecisionProcess <S, A> mdp)
{
IMap <S, A> pi = CollectionFactory.CreateInsertionOrderedMap <S, A>();
ICollection <A> actions = CollectionFactory.CreateQueue <A>();
foreach (S s in mdp.states())
{
actions.Clear();
actions.AddAll(mdp.actions(s));
// Handle terminal states (i.e. no actions).
if (actions.Size() > 0)
{
pi.Put(s, Util.selectRandomlyFromList(actions));
}
}
return(pi);
}
// function POLICY-ITERATION(mdp) returns a policy
/**
* The policy iteration algorithm for calculating an optimal policy.
*
* @param mdp
* an MDP with states S, actions A(s), transition model P(s'|s,a)
* @return an optimal policy
*/
public IPolicy <S, A> policyIteration(IMarkovDecisionProcess <S, A> mdp)
{
// local variables: U, a vector of utilities for states in S, initially
// zero
IMap <S, double> U = Util.create(mdp.states(), 0D);
// π, a policy vector indexed by state, initially random
IMap <S, A> pi = initialPolicyVector(mdp);
bool unchanged;
// repeat
do
{
// U <- POLICY-EVALUATION(π, U, mdp)
U = policyEvaluation.evaluate(pi, U, mdp);
// unchanged? <- true
unchanged = true;
// for each state s in S do
foreach (S s in mdp.states())
{
// calculate:
// max<sub>a ∈ A(s)</sub>
// Σ<sub>s'</sub>P(s'|s,a)U[s']
double aMax = double.NegativeInfinity, piVal = 0;
A aArgmax = pi.Get(s);
foreach (A a in mdp.actions(s))
{
double aSum = 0;
foreach (S sDelta in mdp.states())
{
aSum += mdp.transitionProbability(sDelta, s, a) * U.Get(sDelta);
}
if (aSum > aMax)
{
aMax = aSum;
aArgmax = a;
}
// track:
// Σ<sub>s'</sub>P(s'|s,π[s])U[s']
if (a.Equals(pi.Get(s)))
{
piVal = aSum;
}
}
// if max<sub>a ∈ A(s)</sub>
// Σ<sub>s'</sub>P(s'|s,a)U[s']
// > Σ<sub>s'</sub>P(s'|s,π[s])U[s'] then do
if (aMax > piVal)
{
// π[s] <- argmax<sub>a ∈A(s)</sub>
// Σ<sub>s'</sub>P(s'|s,a)U[s']
pi.Put(s, aArgmax);
// unchanged? <- false
unchanged = false;
}
}
// until unchanged?
} while (!unchanged);
// return π
return(new LookupPolicy <S, A>(pi));
}