def main(): # 初始化 环境 # 冰湖环境 # env = gym.make("FrozenLake-v0", is_slippery=False) # 0 left, 1 down, 2 right, 3 up # env = FrozenLakeWapper(env) # 悬崖环境 env = gym.make("CliffWalking-v0") env = CliffWalkingWapper(env) # 初始化 Agent agent = SarsaAgent(obs_n=env.observation_space.n, act_n=env.action_space.n, learning_rate=0.1, gamma=0.9, e_greed=0.1) # 开始训练 render = False for episode in range(500): ep_steps, ep_reward = run_episode(env, agent, render) print('Episode %s: steps = %s , reward = %.1f' % (episode, ep_steps, ep_reward)) # 每隔 20 个 episode 看一下效果 if episode % 20 == 0: render = True else: render = False # 训练结束,看一下效果 test_episode(env, agent)
def main(): # env = gym.make("FrozenLake-v0", is_slippery=False) # 0 left, 1 down, 2 right, 3 up # env = FrozenLakeWapper(env) env = gym.make("CliffWalking-v0") # 0 up, 1 right, 2 down, 3 left env = CliffWalkingWapper(env) agent = SarsaAgent(obs_n=env.observation_space.n, act_n=env.action_space.n, learning_rate=0.1, gamma=0.9, e_greed=0.1) is_render = False for episode in range(500): ep_reward, ep_steps = run_episode(env, agent, is_render) print('Episode %s: steps = %s , reward = %.1f' % (episode, ep_steps, ep_reward)) # 每隔20个episode渲染一下看看效果 if episode % 20 == 0: is_render = True else: is_render = False # 训练结束,查看算法效果 test_episode(env, agent)
def train(cfg): # env = gym.make("FrozenLake-v0", is_slippery=False) # 0 left, 1 down, 2 right, 3 up # env = FrozenLakeWapper(env) env = gym.make("CliffWalking-v0") # 0 up, 1 right, 2 down, 3 left env = CliffWalkingWapper(env) agent = QLearning(obs_dim=env.observation_space.n, action_dim=env.action_space.n, learning_rate=cfg.policy_lr, gamma=cfg.gamma, epsilon_start=cfg.epsilon_start, epsilon_end=cfg.epsilon_end, epsilon_decay=cfg.epsilon_decay) render = False # 是否打开GUI画面 rewards = [] # 记录所有episode的reward MA_rewards = [] # 记录滑动平均的reward steps = [] # 记录所有episode的steps for i_episode in range(1, cfg.max_episodes + 1): ep_reward = 0 # 记录每个episode的reward ep_steps = 0 # 记录每个episode走了多少step obs = env.reset() # 重置环境, 重新开一局(即开始新的一个episode) while True: action = agent.sample(obs) # 根据算法选择一个动作 next_obs, reward, done, _ = env.step(action) # 与环境进行一个交互 # 训练 Q-learning算法 agent.learn(obs, action, reward, next_obs, done) # 不需要下一步的action obs = next_obs # 存储上一个观察值 ep_reward += reward ep_steps += 1 # 计算step数 if render: env.render() #渲染新的一帧图形 if done: break steps.append(ep_steps) rewards.append(ep_reward) # 计算滑动平均的reward if i_episode == 1: MA_rewards.append(ep_reward) else: MA_rewards.append(0.9 * MA_rewards[-1] + 0.1 * ep_reward) print('Episode %s: steps = %s , reward = %.1f, explore = %.2f' % (i_episode, ep_steps, ep_reward, agent.epsilon)) # 每隔20个episode渲染一下看看效果 if i_episode % 20 == 0: render = True else: render = False agent.save() # 训练结束,保存模型 output_path = os.path.dirname(__file__) + "/result/" # 检测是否存在文件夹 if not os.path.exists(output_path): os.mkdir(output_path) np.save(output_path + "rewards_train.npy", rewards) np.save(output_path + "MA_rewards_train.npy", MA_rewards) np.save(output_path + "steps_train.npy", steps)
def test(cfg): env = gym.make("CliffWalking-v0") # 0 up, 1 right, 2 down, 3 left env = CliffWalkingWapper(env) agent = QLearning(obs_dim=env.observation_space.n, action_dim=env.action_space.n, learning_rate=cfg.policy_lr, gamma=cfg.gamma, epsilon_start=cfg.epsilon_start, epsilon_end=cfg.epsilon_end, epsilon_decay=cfg.epsilon_decay) agent.load() # 导入保存的模型 rewards = [] # 记录所有episode的reward MA_rewards = [] # 记录滑动平均的reward steps = [] # 记录所有episode的steps for i_episode in range(1, 10 + 1): ep_reward = 0 # 记录每个episode的reward ep_steps = 0 # 记录每个episode走了多少step obs = env.reset() # 重置环境, 重新开一局(即开始新的一个episode) while True: action = agent.predict(obs) # 根据算法选择一个动作 next_obs, reward, done, _ = env.step(action) # 与环境进行一个交互 obs = next_obs # 存储上一个观察值 time.sleep(0.5) env.render() ep_reward += reward ep_steps += 1 # 计算step数 if done: break steps.append(ep_steps) rewards.append(ep_reward) # 计算滑动平均的reward if i_episode == 1: MA_rewards.append(ep_reward) else: MA_rewards.append(0.9 * MA_rewards[-1] + 0.1 * ep_reward) print('Episode %s: steps = %s , reward = %.1f' % (i_episode, ep_steps, ep_reward)) plt.plot(MA_rewards) plt.show()
action = np.random.choice(action_list) # 最大值随机选取 return action def learn(self, state, action, reward, next_state, next_action, done): predict_Q = self.Q_TABLE[state, action] # Q_table表找到对应Q的评估值 if done: target_Q = reward else: # Q <- Q + a*[(R + y*next_Q) - Q] Q_TABLE预测 max_action(Q(s',a)) target_Q = reward + GAMMA * np.max(self.Q_TABLE[next_state,:]) self.Q_TABLE[state, action] = self.Q_TABLE[state, action] + LEARNING_RATE * (target_Q - predict_Q) if __name__ == '__main__': env = gym.make("CliffWalking-v0") env = CliffWalkingWapper(env) dim_state = env.observation_space.n # 48 dim_action = env.action_space.n # 4 agent = QLearningAgent(dim_state, dim_action) for epoch in range(500): state = env.reset() # 开始一局游戏 total_rewards, total_steps = 0, 0 action = agent.choose_action(state) while True: #env.render() next_state, reward, done, _ = env.step(action) # 采取该行为获取下一个state 及分数 next_action = agent.choose_action(next_state) # 行为 概率 agent.learn(state, action, reward, next_state, next_action, done) action = next_action