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https://github.com/wangshusen/DRL

Deep Reinforcement Learning
https://github.com/wangshusen/DRL

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Deep Reinforcement Learning

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README

        

# Deep Reinforcement Learning

1. **Overview.**

* Reinforcement Learning
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/1_Basics_1.pdf)]
[[lecture note](https://github.com/wangshusen/DeepLearning/blob/master/LectureNotes/DRL/DRL.pdf)]
[[Video (in Chinese)](https://youtu.be/vmkRMvhCW5c)].

* Value-Based Learning
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/1_Basics_2.pdf)]
[[Video (in Chinese)](https://youtu.be/jflq6vNcZyA)].

* Policy-Based Learning
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/1_Basics_3.pdf)]
[[Video (in Chinese)](https://youtu.be/qI0vyfR2_Rc)].

* Actor-Critic Methods
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/1_Basics_4.pdf)]
[[Video (in Chinese)](https://youtu.be/xjd7Jq9wPQY)].

* AlphaGo
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/1_Basics_5.pdf)]
[[Video (in Chinese)](https://youtu.be/zHojAp5vkRE)].

2. **TD Learning.**

* Sarsa
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/2_TD_1.pdf)]
[[Video (in Chinese)](https://youtu.be/-cYWdUubB6Q)].

* Q-learning
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/2_TD_2.pdf)]
[[Video (in Chinese)](https://youtu.be/Ymy2w3DGn2U)].

* Multi-Step TD Target
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/2_TD_3.pdf)]
[[Video (in Chinese)](https://youtu.be/UqTP138IATc)].


3. **Advanced Topics on Value-Based Learning.**

* Experience Replay (ER) & Prioritized ER
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/3_DQN_1.pdf)]
[[Video (in Chinese)](https://youtu.be/rhslMPmj7SY)].

* Overestimation, Target Network, & Double DQN
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/3_DQN_2.pdf)]
[[Video (in Chinese)](https://youtu.be/X2-56QN79zc)].

* Dueling Networks
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/3_DQN_3.pdf)]
[[Video (in Chinese)](https://youtu.be/DBux6cA0EoM)].

4. **Policy Gradient with Baseline.**

* Policy Gradient with Baseline
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/4_Policy_1.pdf)]
[[Video (in Chinese)](https://youtu.be/yNEqbptitZs)].

* REINFORCE with Baseline
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/4_Policy_2.pdf)]
[[Video (in Chinese)](https://youtu.be/Ob78ADXTQNo)].

* Advantage Actor-Critic (A2C)
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/4_Policy_3.pdf)]
[[Video (in Chinese)](https://youtu.be/mtT4TSGSon8)].

* REINFORCE versus A2C
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/4_Policy_4.pdf)]
[[Video (in Chinese)](https://youtu.be/hN9WMIMMeAI)].

5. **Advanced Topics on Policy-Based Learning.**

* Trust-Region Policy Optimization (TRPO)
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/5_Policy_1.pdf)]
[[Video (in Chinese)](https://youtu.be/fcSYiyvPjm4)].

* Partial Observation and RNNs.

6. **Dealing with Continuous Action Space.**

* Discrete versus Continuous Control
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/6_Continuous_1.pdf)]
[[Video (in Chinese)](https://youtu.be/rRIjgdxSvg8)].

* Deterministic Policy Gradient (DPG) for Continuous Control
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/6_Continuous_2.pdf)]
[[Video (in Chinese)](https://youtu.be/cmWejKRWLA8)].

* Stochastic Policy Gradient for Continuous Control
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/6_Continuous_3.pdf)]
[[Video (in Chinese)](https://youtu.be/McqFyl_W5Wc)].

7. **Multi-Agent Reinforcement Learning.**

* Basics and Challenges
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/7_MARL_1.pdf)]
[[Video (in Chinese)](https://youtu.be/KN-XMQFTD0o)].

* Centralized VS Decentralized
[[slides](https://github.com/wangshusen/DRL/blob/master/Slides/7_MARL_2.pdf)]
[[Video (in Chinese)](https://youtu.be/0HV1hsjd1y8)].

8. **Imitation Learning.**

* Inverse Reinforcement Learning.

* Generative Adversarial Imitation Learning (GAIL).