def best_policy(mdp, U):
    """Given an MDP and a utility function U, determine the best policy,
    as a mapping from state to action. (Equation 17.4)"""
    pi = {}
    for s in mdp.states:
        pi[s] = argmax(mdp.actions(s), lambda a:expected_utility(a, s, U, mdp))
    return pi
def best_policy(mdp, U):
    """Given an MDP and a utility function U, determine the best policy,
    as a mapping from state to action. (Equation 17.4)"""
    pi = {}
    for s in mdp.states:
        pi[s] = argmax(mdp.actions(s), lambda a:expected_utility(a, s, U, mdp))
    return pi
def best_policy ( mdp , U ) : pi = { } for s in mdp . states : pi [ s ] = argmax ( mdp . actions ( s ) , lambda a : expected_utility ( a , s , U , mdp ) ) return pi
Given an MDP and a utility function U, determine the best policy,
    as a mapping from state to action. (Equation 17.4)
Given an MDP and a utility function U determine the best policy as a mapping from state to action . ( Equation 17 . 4 )