aima/mdp.py
def value_iteration(mdp, epsilon=0.001):
    "Solving an MDP by value iteration. [Fig. 17.4]"
    U1 = dict([(s, 0) for s in mdp.states])
    R, T, gamma = mdp.R, mdp.T, mdp.gamma
    while True:
        U = U1.copy()
        delta = 0
        for s in mdp.states:
            U1[s] = R(s) + gamma * max([sum([p * U[s1] for (p, s1) in T(s, a)])
                                        for a in mdp.actions(s)])
            delta = max(delta, abs(U1[s] - U[s]))
        if delta < epsilon * (1 - gamma) / gamma:
             return U
def value_iteration(mdp, epsilon=0.001):
    "Solving an MDP by value iteration. [Fig. 17.4]"
    U1 = dict([(s, 0) for s in mdp.states])
    R, T, gamma = mdp.R, mdp.T, mdp.gamma
    while True:
        U = U1.copy()
        delta = 0
        for s in mdp.states:
            U1[s] = R(s) + gamma * max([sum([p * U[s1] for (p, s1) in T(s, a)])
                                        for a in mdp.actions(s)])
            delta = max(delta, abs(U1[s] - U[s]))
        if delta < epsilon * (1 - gamma) / gamma:
             return U
def value_iteration ( mdp , epsilon = 0.001 ) : U1 = dict ( [ ( s , 0 ) for s in mdp . states ] ) R , T , gamma = mdp . R , mdp . T , mdp . gamma while True : U = U1 . copy ( ) delta = 0 for s in mdp . states : U1 [ s ] = R ( s ) + gamma * max ( [ sum ( [ p * U [ s1 ] for ( p , s1 ) in T ( s , a ) ] ) for a in mdp . actions ( s ) ] ) delta = max ( delta , abs ( U1 [ s ] - U [ s ] ) ) if delta < epsilon * ( 1 - gamma ) / gamma : return U
Solving an MDP by value iteration. [Fig. 17.4]
Solving an MDP by value iteration . [ Fig . 17 . 4 ]