def main():
RENDER = False
MAX_EXPLORE = 2000
# env = gym.make('MountainCar-v0')
env = gym.make('CartPole-v0')
env.seed(1)
env = env.unwrapped
print(f"action_space: {env.action_space}")
print(f"action_space.n: {env.action_space.n}")
print(f"observation_space: {env.observation_space}")
print(f"observation_space.shape: {env.observation_space.shape}")
print(f"observation_space.high: {env.observation_space.high}")
print(f"observation_space.low: {env.observation_space.low}")
# action_space: Discrete(3)
# action_space.n: 3
# observation_space:
# Box(-1.2000000476837158, 0.6000000238418579, (2,), float32)
# observation_space.shape: (2,)
# observation_space.high: [0.6 0.07]
# observation_space.low: [-1.2 -0.07]
agent = PolicyGradient(n_features=env.observation_space.shape[0],
n_actions=env.action_space.n,
lr=0.001,
reward_decay=0.995)
episodes = 3000
total_reward = []
for episode in range(episodes):
s = env.reset()
# s : list
# s.shape = (2,)
for i in range(MAX_EXPLORE):
if RENDER:
env.render()
a = agent.choose_action(s)
# a : scalar
s_, r, done, _ = env.step(a)
# s : list, shape=(2,)
# r : float
# done : bool
agent.store_transition(s, a, r)
if done or (i + 1) == MAX_EXPLORE:
ep_rs_sum = sum(agent.ep_r)
total_reward.append(ep_rs_sum)
avg_reward = sum(total_reward) / len(total_reward)
print(f"Episode: {episode+1}")
print(f"\treward: {ep_rs_sum}, done: {done}")
print(f"\tavg reward: {avg_reward}")
vt = agent.learn()
if avg_reward > 200:
RENDER = True
if episode == 30:
plt.plot(vt)
plt.xlabel('episode steps')
plt.ylabel('normalized state-action value')
plt.show()
break
s = s_
learning_rate=0.02,
reward_decay=0.99,
)
running_reward = 0
for i_episode in range(3000):
observation = env.reset()
while True:
if RENDER: env.render()
action = RL.choose_action(observation)
observation_, reward, done, info = env.step(action)
RL.store_reward(reward)
ep_rs_sum = sum(RL.ep_rs)
if done or ep_rs_sum > 2 * DISPLAY_REWARD_THRESHOLD: # End the ep if done or get enough reward
running_reward = running_reward * 0.9 + ep_rs_sum * 0.1
if running_reward > DISPLAY_REWARD_THRESHOLD: RENDER = True
print("Episode:", i_episode, " Reward:", int(running_reward))
vt = RL.learn()
break
observation = observation_
if running_reward > 2 * DISPLAY_REWARD_THRESHOLD: break