RL.saver.restore(RL.sess, model_file)
max_reward = -200
for i_episode in range(1000):
observation = env.reset()
running_reward = 0
i = 0
while True:
if RENDER:
env.render()
action = RL.choose_action(observation)
observation_, reward, done, info = env.step(action)
RL.store_transition(observation, action, reward)
i += 1
if i % 1000 == 0:
print("i=%d, action=%d" % (i, action))
if done:
ep_rs_sum = sum(RL.ep_rs)
if 'running_reward' not in globals():
running_reward = ep_rs_sum
else:
running_reward = running_reward * 0.99 + ep_rs_sum * 0.01
if running_reward > DISPLAY_REWARD_THRESHOLD:
RENDER = True
def main():
env = grc.RemoteEnv('tmp/sock')
# Policy gradient has high variance, seed for reproducability
env.seed(1)
RENDER_ENV = False
rewards = []
INITIAL_EPSILON = 0.7
EPSILON_GREEDY_INCREMENT = 0.01
# Load checkpoint
load_version = "2018-06-05 18:24:13"
timestamp = strftime("%Y-%m-%d %H:%M:%S", gmtime())
load_path = "output/model2/{}/SonicTheHedgehog.ckpt".format(load_version)
PG = PolicyGradient(
n_x = [112,112,3], #env.observation_space.shape,
n_y = env.action_space.n,
learning_rate=0.02,
reward_decay=0.99,
load_path=load_path,
epsilon_max=0.98,
epsilon_greedy_increment=EPSILON_GREEDY_INCREMENT,
initial_epsilon = INITIAL_EPSILON
)
observation = env.reset()
# print("obs", observation)
episode_reward = 0
tic = time.clock()
while True:
if RENDER_ENV: env.render()
# 1. Choose an action based on observation
observation = observation[:,96:,:] # make square, keep right sight of image
observation = observation[::2,::2,:] # downsample to [112,112,3]
observation = observation / 255 # normalize
action = PG.choose_action(observation)
# 2. Take action in the environment
observation_, reward, done, info = env.step(action)
# 4. Store transition for training
PG.store_transition(observation, action, reward)
episode_rewards_sum = sum(PG.episode_rewards)
toc = time.clock()
elapsed_sec = toc - tic
# Save new observation
observation = observation_
if done:
episode_rewards_sum = sum(PG.episode_rewards)
rewards.append(episode_rewards_sum)
max_reward_so_far = np.amax(rewards)
if episode_rewards_sum == 0.0:
print("-----------------------------------")
print("Backtrack epsilon for more exploration...")
PG.epsilon = max(PG.epsilon - EPSILON_GREEDY_INCREMENT, INITIAL_EPSILON)
print("==========================================")
print("Epsilon: ", PG.epsilon)
print("Seconds: ", elapsed_sec)
print("Reward: ", episode_rewards_sum)
print("Max reward so far: ", max_reward_so_far)
# 5. Train neural network
tic = time.clock()
discounted_episode_rewards_norm = PG.learn()
toc = time.clock()
elapsed_sec = toc - tic
print("Train Seconds: ", elapsed_sec)
observation = env.reset()
这里先用env.unwrapper来移除env上包裹的所有wrapper（其中一个让env的每个episode最大step设置为200），
然后给env包裹上Monitor Wrapper来实现渲染
"""
env = env.unwrapped
env = wrappers.Monitor(env, "../gym-results", force=True)
for episode in range(1000):
obs = env.reset()
while True:
if RENDER:
env.render()
action = pg.choose_action(obs)
obs_, reward, done, info = env.step(action)
# print("cur_reward = ", reward)
pg.store_transition(obs, action, reward)
if done:
ep_rewards_sum = sum(pg.ep_rewards)
if 'running_reward' not in globals():
running_reward = ep_rewards_sum
else:
running_reward = running_reward * 0.99 + ep_rewards_sum * 0.01
if running_reward > DISPLAY_REWARD_THRESHOLD:
RENDER = True
print("episode: ", episode, " reward: ", running_reward)
vt = pg.learn()
# if episode == 30:
# plt.plot(vt)
# plt.xlabel("episode")