def greedyQPolicy(Qs):
""" Find the greedy deterministic policy,
given the Q-values. """
dim = len(Qs)
numA = len(Qs[0])
policy = zeros((dim, numA))
for si in range(dim):
actions = all_argmax(Qs[si])
for a in actions:
policy[si, a] = 1. / len(actions)
return policy
def greedyQPolicy(Qs):
""" Find the greedy deterministic policy,
given the Q-values. """
dim = len(Qs)
numA = len(Qs[0])
policy = zeros((dim, numA))
for si in range(dim):
actions = all_argmax(Qs[si])
for a in actions:
policy[si, a] = 1. / len(actions)
return policy
def greedyPolicy(Ts, R, discountFactor, V):
""" Find the greedy policy, (soft tie-breaking)
given a value function and full transition model. """
dim = len(V)
numA = len(Ts)
Vnext = V * discountFactor + R
policy = zeros((dim, numA))
for si in range(dim):
actions = all_argmax([dot(T[si, :], Vnext) for T in Ts])
for a in actions:
policy[si, a] = 1. / len(actions)
return policy, collapsedTransitions(Ts, policy)
def greedyPolicy(Ts, R, discountFactor, V):
""" Find the greedy policy, (soft tie-breaking)
given a value function and full transition model. """
dim = len(V)
numA = len(Ts)
Vnext = V*discountFactor+R
policy = zeros((dim, numA))
for si in range(dim):
actions = all_argmax([dot(T[si, :], Vnext) for T in Ts])
for a in actions:
policy[si, a] = 1. / len(actions)
return policy, collapsedTransitions(Ts, policy)
