break
return states, actions, rewards
blackjack = Blackjack ()
average_policy = np.zeros ((2, 21, 11), dtype=np.float)
for k in range (1001) :
policy = np.ones ((2, 21, 11))
policy[:, 18:21, :] = 0
q_values = np.zeros ((2, 21, 11, 2))
for i in range (5000) :
initial_state = blackjack.new_state ()
states, actions, rewards = generate_episode (initial_state, policy)
G = 0
for j in range (len(actions)-1, -1, -1) :
G = G + rewards[j+1]
q_values[states[j][0], states[j][1], states[j][2], int (actions[j])] = 0.1 * q_values[states[j][0], states[j][1], states[j][2], int (actions[j])] + 0.9 * G
policy = np.argmax (q_values, axis=-1)
average_policy += policy
average_policy /= 1001
average_policy = np.round (average_policy, decimals=2)
