高级机器学习-作业5

在CS294-112 HW3基础上实现了Double DQN算法

目录

• data目录下保存的是不同参数下的训练奖励日志
• video目录下保存的是不同参数下的算法训练过程视频

使用

• 运行run_dqn_atari.py训练强化学习算法
• 运行plot.py绘制奖励曲线

以下是原仓库README

---

CS294-112 HW 3: Q-Learning

Dependencies:

• Python 3.5
• Numpy version 1.14.5
• TensorFlow version 1.10.5
• MuJoCo version 1.50 and mujoco-py 1.50.1.56
• OpenAI Gym version 0.10.5
• seaborn
• Box2D==2.3.2
• OpenCV
• ffmpeg

Before doing anything, first replace gym/envs/box2d/lunar_lander.py with the provided lunar_lander.py file.

The only files that you need to look at are dqn.py and train_ac_f18.py, which you will implement.

See the HW3 PDF for further instructions.

The starter code was based on an implementation of Q-learning for Atari generously provided by Szymon Sidor from OpenAI.