92 lines
3.0 KiB
Python
92 lines
3.0 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# -*- coding: utf-8 -*-
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import gym
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from gridworld import CliffWalkingWapper, FrozenLakeWapper
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from agent import SarsaAgent
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import time
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def run_episode(env, agent, render=False):
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total_steps = 0 # 记录每个episode走了多少step
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total_reward = 0
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obs = env.reset() # 重置环境, 重新开一局（即开始新的一个episode）
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action = agent.sample(obs) # 根据算法选择一个动作
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while True:
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next_obs, reward, done, _ = env.step(action) # 与环境进行一个交互
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next_action = agent.sample(next_obs) # 根据算法选择一个动作
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# 训练 Sarsa 算法
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agent.learn(obs, action, reward, next_obs, next_action, done)
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action = next_action
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obs = next_obs # 存储上一个观察值
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total_reward += reward
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total_steps += 1 # 计算step数
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if render:
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env.render() #渲染新的一帧图形
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if done:
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break
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return total_reward, total_steps
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def test_episode(env, agent):
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total_reward = 0
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obs = env.reset()
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while True:
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action = agent.predict(obs) # greedy，只取最优的动作
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next_obs, reward, done, _ = env.step(action)
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total_reward += reward
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obs = next_obs
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time.sleep(0.5) # 每个step延迟0.5秒来看看效果
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env.render()
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if done:
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print('test reward = %.1f' % (total_reward))
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break
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def main():
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# env = gym.make("FrozenLake-v0", is_slippery=False) # 0 left, 1 down, 2 right, 3 up
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# env = FrozenLakeWapper(env)
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env = gym.make("CliffWalking-v0") # 0 up, 1 right, 2 down, 3 left
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env = CliffWalkingWapper(env) # 这行不加也可以，这个是为了显示效果更好一点
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agent = SarsaAgent(
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obs_n=env.observation_space.n,
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act_n=env.action_space.n,
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learning_rate=0.1,
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gamma=0.9,
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e_greed=0.1)
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is_render = False
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for episode in range(500):
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ep_reward, ep_steps = run_episode(env, agent, is_render)
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print('Episode %s: steps = %s , reward = %.1f' % (episode, ep_steps,
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ep_reward))
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# 每隔20个episode渲染一下看看效果（每个episode都渲染的话，时间会比较长）
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if episode % 20 == 0:
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is_render = True
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else:
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is_render = False
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# 训练结束，查看算法效果
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test_episode(env, agent)
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if __name__ == "__main__":
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main() |