RL = PolicyGradient(
n_actions=env.action_space.n,
n_features=env.observation_space.shape[0],
learning_rate=0.02,
reward_decay=0.995,
# output_graph=True,
)
for eposide_i in range(1000):
observation = env.reset()
while True:
if RENDER: env.render()
action = RL.choose_action(observation)
observation_, reward, done, info = env.step(action)
RL.store_transition(observation, action, reward)
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 = False #True # rendering
print("episode:", eposide_i, " reward:", int(running_reward))
RL = PolicyGradient(
n_actions=env.action_space.n,
n_features=env.observation_space.shape[0],
learning_rate=0.02,
reward_decay=0.995,
# output_graph=True,
)
for i_episode in range(1000):
observation = env.reset()
while True:
if RENDER: env.render()
action = RL.choose_action(observation)
observation_, reward, done, info = env.step(action) # reward = -1 in all cases
RL.store_transition(observation, action, reward)
if done:
# calculate running reward
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 # rendering
print("episode:", i_episode, " reward:", int(running_reward))
print("n_features=", RL.n_features)
i_episode = 0
# for i_episode in range(60000):
while True:
i_episode += 1
state = env.reset()
done = False
user = 0
reward1 = reward2 = 0
while not done:
if user == 0:
action1 = RL.choose_action(state)
state1, reward1, done, infos = env.step(action1, -1)
if done:
RL.store_transition(state, action1, reward1)
state = state1
reward1 = reward2 = 0
elif user == 1:
while True:
random_act = env.action_space.sample()
x = random_act % 3
y = random_act // 3
found = False
for i in range(0, 27, 3):
chunk = state1[i : i + 3]
# print("chunk=",chunk)
if ([x,y,1] == chunk).all():
for i_episode in range(600):
if i_episode in range(0, 300):
env = relay_net_slow
#env,dummy= create_example_env()
state, reward = env.update_state()
state = np.array(state)
if i_episode in range(300, 600):
env = relay_net_Reduce
state, reward = env.update_state()
state = np.array(state)
for i in range(steps_per_episode):
action_id = RL.choose_action(state)
action_space = env.get_possible_action_space()
action = action_space[action_id]
if random_action:
action = action_space[np.random.randint(len(action_space))]
env.apply_action(action)
next_state, reward = env.update_state()
next_state = np.array(next_state)
reward = postprocessreward(reward, th)
RL.store_transition(state, action_id, reward)
state = next_state
if reward > 1:
n_actions=env.action_space.n,
n_features=env.observation_space.shape[0],
learning_rate=0.02,
reward_decay=0.99,
# output_graph=True,
)
for i_episode in range(3000):
observation = env.reset()
while True:
if RENDER: env.render()
action = RL.choose_action(
observation
) #agent根据策略\pi进行探索,直到探索结束. 一轮探索的所有结果<observation, action, reward>存储在记忆库中,用于训练
observation_, reward, done, info = env.step(
action) #所以policy gradient是非常耗时的,大多数时间都花费在与环境交互上
RL.store_transition(observation, action,
reward) #每一轮探索都会将相关的东西存储到replay
if done:
ep_rs_sum = sum(RL.ep_rs) #所有奖励值之和
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 # rendering
