def eval(cfg, saved_model_path=SAVED_MODEL_PATH):
print('start to eval ! \n')
device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
env = gym.make('CartPole-v0').unwrapped # 可google为什么unwrapped gym，此处一般不需要
env.seed(1) # 设置env随机种子
n_states = env.observation_space.shape[0]
n_actions = env.action_space.n
agent = DQN(n_states=n_states,
n_actions=n_actions,
device="cpu",
gamma=cfg.gamma,
epsilon_start=cfg.epsilon_start,
epsilon_end=cfg.epsilon_end,
epsilon_decay=cfg.epsilon_decay,
policy_lr=cfg.policy_lr,
memory_capacity=cfg.memory_capacity,
batch_size=cfg.batch_size)
agent.load_model(saved_model_path + 'checkpoint.pth')
rewards = []
moving_average_rewards = []
ep_steps = []
log_dir = os.path.split(
os.path.abspath(__file__))[0] + "/logs/eval/" + SEQUENCE
writer = SummaryWriter(log_dir)
for i_episode in range(1, cfg.eval_eps + 1):
state = env.reset() # reset环境状态
ep_reward = 0
for i_step in range(1, cfg.eval_steps + 1):
action = agent.choose_action(state,
train=False) # 根据当前环境state选择action
next_state, reward, done, _ = env.step(action) # 更新环境参数
ep_reward += reward
state = next_state # 跳转到下一个状态
if done:
break
print('Episode:', i_episode, ' Reward: %i' % int(ep_reward),
'n_steps:', i_step, 'done: ', done)
ep_steps.append(i_step)
rewards.append(ep_reward)
# 计算滑动窗口的reward
if i_episode == 1:
moving_average_rewards.append(ep_reward)
else:
moving_average_rewards.append(0.9 * moving_average_rewards[-1] +
0.1 * ep_reward)
writer.add_scalars('rewards', {
'raw': rewards[-1],
'moving_average': moving_average_rewards[-1]
}, i_episode)
writer.add_scalar('steps_of_each_episode', ep_steps[-1], i_episode)
writer.close()
'''存储reward等相关结果'''
save_results(rewards,
moving_average_rewards,
ep_steps,
tag='eval',
result_path=RESULT_PATH)
print('Complete evaling！')
def train(cfg):
print('Start to train !')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
env = gym.make('CartPole-v0').unwrapped # 可google为什么unwrapped gym，此处一般不需要
env.seed(1) # 设置env随机种子
n_states = env.observation_space.shape[0]
n_actions = env.action_space.n
agent = DQN(n_states=n_states, n_actions=n_actions, device=device, gamma=cfg.gamma, epsilon_start=cfg.epsilon_start,
epsilon_end=cfg.epsilon_end, epsilon_decay=cfg.epsilon_decay, policy_lr=cfg.policy_lr, memory_capacity=cfg.memory_capacity, batch_size=cfg.batch_size)
rewards = []
moving_average_rewards = []
ep_steps = []
log_dir=os.path.split(os.path.abspath(__file__))[0]+"/logs/train/" + SEQUENCE
writer = SummaryWriter(log_dir)
for i_episode in range(1, cfg.train_eps+1):
state = env.reset() # reset环境状态
ep_reward = 0
for i_step in range(1, cfg.train_steps+1):
action = agent.choose_action(state) # 根据当前环境state选择action
next_state, reward, done, _ = env.step(action) # 更新环境参数
ep_reward += reward
agent.memory.push(state, action, reward, next_state, done) # 将state等这些transition存入memory
state = next_state # 跳转到下一个状态
agent.update() # 每步更新网络
if done:
break
# 更新target network，复制DQN中的所有weights and biases
if i_episode % cfg.target_update == 0:
agent.target_net.load_state_dict(agent.policy_net.state_dict())
print('Episode:', i_episode, ' Reward: %i' %
int(ep_reward), 'n_steps:', i_step, 'done: ', done,' Explore: %.2f' % agent.epsilon)
ep_steps.append(i_step)
rewards.append(ep_reward)
# 计算滑动窗口的reward
if i_episode == 1:
moving_average_rewards.append(ep_reward)
else:
moving_average_rewards.append(
0.9*moving_average_rewards[-1]+0.1*ep_reward)
writer.add_scalars('rewards',{'raw':rewards[-1], 'moving_average': moving_average_rewards[-1]}, i_episode)
writer.add_scalar('steps_of_each_episode',
ep_steps[-1], i_episode)
writer.close()
print('Complete training！')
''' 保存模型 '''
if not os.path.exists(SAVED_MODEL_PATH): # 检测是否存在文件夹
os.mkdir(SAVED_MODEL_PATH)
agent.save_model(SAVED_MODEL_PATH+'checkpoint.pth')
print('model saved！')
'''存储reward等相关结果'''
save_results(rewards,moving_average_rewards,ep_steps,tag='train',result_path=RESULT_PATH)
print('Accept new connection from %s:%s...' % addr)
agent = DQN(pretrained=True)
state = torch.zeros((150, 6), device=device, dtype=torch.float)
state[0][5] = 0.26
state[0][1] = 4.75
state = state.unsqueeze(0)
reward = 0
for i in range(6005):
if i == 0:
action = 50
#action = torch.zeros((1),device=device,dtype=torch.float,requires_grad=False)
else:
action = agent.choose_action(state)
msg = client_executor.recv(16384).decode('utf-8')
client_executor.send(bytes(str(action / 10 - 5).encode('utf-8')))
next_state, new_reward, done = data_clean(msg)
add_reward = new_reward - reward
reward = new_reward
agent.memory.push(state, action, add_reward, next_state, done)
state = next_state
start = time.time()
agent.update() # 每步更新网络
end = time.time()
if (i % 200 == 199):
save_model(agent, model_path=SAVED_MODEL_PATH)
print("save", i)
print(str(action / 10 - 5), end - start)
