returns_sum = defaultdict(float)
returns_count = defaultdict(float)
V = defaultdict(float)
num_episodes = 10000
t_steps = 100
R = np.zeros((num_episodes, 1))
for i_episode in range(1, num_episodes + 1):
episode = []
state = env.reset()
rewards = 0.0
for t in range(t_steps):
action = policy(state)
next_state, reward, done, _ = env.step(action)
episode.append((state, action, reward))
if done:
break
state = next_state
states_in_episode = set([s for s, _, _ in episode])
for s in states_in_episode:
first_occurence_idx = next(i for i, x in enumerate(episode)
if x[0] == state)
G = sum([
r * (discount_factor**i)
for i, (_, _, r) in enumerate(episode[first_occurence_idx:])
])
returns_sum[state] += G
if "../" not in sys.path:
sys.path.append("../")
from lib.envs.blackjack import BlackjackEnv
env=BlackjackEnv()
def print_observation(observation):
score, dealer_score, usable_ace = observation
print("Player Score: {} (Usable Ace: {}), Dealer Score: {}".format(
score, usable_ace, dealer_score))
def strategy(observation):
score, dealer_score, usable_ace = observation
# Stick (action 0) if the score is > 20, hit (action 1) otherwise
return 0 if score >= 20 else 1
for i_episode in range(20):
observation = env.reset()
for t in range(100):
print_observation(observation)
action = strategy(observation)
print("Taking action: {}".format( ["Stick", "Hit"][action]))
observation, reward, done, _ = env.step(action)
if done:
print_observation(observation)
print("Game end. Reward: {}\n".format(float(reward)))
break
