def test_mc_control(env):
# obtain the estimated optimal policy and action-value function
policy, Q = MC_control.mc_control(env, 500000, 0.02)
# obtain the corresponding state-value function
V = dict((k, np.max(v)) for k, v in Q.items())
# plot the state-value function
plot_blackjack_values(V)
# plot the policy
plot_policy(policy)
def mc_control(env, num_episodes, alpha, gamma=1.0, eps=0.8, min_eps=0.05):
nA = env.action_space.n
Q = defaultdict(lambda: np.zeros(nA))
policy = {}
for i_episode in range(1, num_episodes + 1):
if i_episode % 1000 == 0:
eps *= 0.98
print("\rEpisode {}/{}.".format(i_episode, num_episodes), end="")
episode = episode_eps_greedy(env, max(eps, min_eps), policy)
states, actions, rewards = zip(*episode)
G = 0
for i in range(len(episode)):
G += rewards[i] * (gamma**i)
Q[states[i]][actions[i]] = (
(1 - alpha) * Q[states[i]][actions[i]]) + (alpha * G)
for s in Q:
policy.update({s: np.argmax(Q[s])})
return policy, Q
env = gym.make('Blackjack-v0')
print(env.observation_space)
print(env.action_space)
policy, Q = mc_control(env, 200000, 0.05)
V = dict((k, np.max(v)) for k, v in Q.items())
plot_blackjack_values(V)
plot_policy(policy)
# obtain the state-value function V_glie = dict((k,np.max(v)) for k, v in Q_glie.items()) # plot the state-value function plot_blackjack_values(V_glie) # Finally, we visualize the policy that is estimated to be optimal. # In[33]: from plot_utils import plot_policy # plot the policy plot_policy(policy_glie) # The **true** optimal policy $\pi_*$ can be found on page 82 of the [textbook](http://go.udacity.com/rl-textbook) (and appears below). Compare your final estimate to the optimal policy - how close are you able to get? If you are not happy with the performance of your algorithm, take the time to tweak the decay rate of $\epsilon$ and/or run the algorithm for more episodes to attain better results. # #  # ### Part 4: MC Control: Constant-$\alpha$ # # In this section, you will write your own implementation of constant-$\alpha$ MC control. # # Your algorithm has four arguments: # - `env`: This is an instance of an OpenAI Gym environment. # - `num_episodes`: This is the number of episodes that are generated through agent-environment interaction. # - `alpha`: This is the step-size parameter for the update step. # - `gamma`: This is the discount rate. It must be a value between 0 and 1, inclusive (default value: `1`).