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deeprl-chinese's Introduction

介绍

这里是《深度强化学习》的主要算法实现。为了方便阅读,单个算法的实现及调用放在一个文件中。调用方式简单:

mkdir -p output
python -u 04_dqn.py --do_train --output_dir output 2>&1 | tee output/log.txt

环境

根据动作状态空间是否连续,我们考虑两种环境:

测试环境是python3.7,依赖安装:

pip install -r requirements.txt 

所有代码均用于教学,可在笔记本CPU环境下训练。

算法列表

章节 算法
1 机器学习基础 MNIST
2 蒙特卡洛 PI approximation
3 强化学习基本概念 CartPole
4 DQN与Q学习 DQN
5 SARSA算法 SARSA
6 价值学习与高级技巧 Dueling DQN, Double DQN
7 策略梯度算法 REINFORCE, Actor Critic
8 带基线的策略梯度方法 REINFORCE with baseline, A2C
9 策略学习高级技巧 TRPO
10 连续控制 DDPG, TD3
11 对状态的不完全观测
12 模仿学习 GAIL
13 并行计算 A3C
14 多智能体系统 MPE
15 合作关系设定下的多智能体强化学习 MAC-A2C
16 非合作关系设定下的多智能体强化学习
17 注意力机制与多智能体强化学习
18 AlphaGo 与蒙特卡洛树搜索 AlphaZero
19 现实世界中的应用 NAS Recommender

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