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rl-paper-review

★ 47 Open GitHub ↗

road-map & paper review for Reinforcement Learning

// repository documentation

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# rl-paper-review

### Reinforcement Learning Roadmap

[`GITMIND`](https://gitmind.com/app/doc/e3a1343403) < Link!!

![rl-roadmap](https://raw.githubusercontent.com/dion-jy/rl-paper-review/master/rl-roadmap.png)

---

### Policy Gradient

**(1) Vanila PG(Sutton)**

[Policy gradient methods for reinforcement learning with function approximation]

Richard S. Sutton, David McAllester, Satinder Singh, Yishay Mansour,1994

[`REVIEW`](./reviews/Sutton_PG.md)	|	[`PAPER`](http://papers.nips.cc/paper/1713-policy-gradient-methods-for-reinforcement-learning-with-function-approximation.pdf)

**(2) DPG**

[Deterministic policy gradient algorithms]

Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., & Riedmiller, M. (2014).

[`REVIEW`](./reviews/DPG.md)	|	[`PAPER`](http://proceedings.mlr.press/v32/silver14.pdf)

**(3) DDPG**

[Continuous control with deep reinforcement learning]

Timothy P. Lillicrap∗ , Jonathan J. Hunt∗ , Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver & Daan Wierstra (2016)

[`REVIEW`](./reviews/DDPG.md)	|	[`PAPER`](https://arxiv.org/pdf/1509.02971.pdf)	|	[`CODE`](https://github.com/CUN-bjy/walkyto-ddpg)

**(4) NPG**

[A natural policy gradient]

Sham Kakade(2002)

[`REVIEW`](./reviews/NPG.md)	|	[`PAPER`](https://papers.nips.cc/paper/2073-a-natural-policy-gradient.pdf)

**(5) TRPO**

[Trust region policy optimization]

John Schulman, Sergey Levine, Philipp Moritz, Michael Jordan, Pieter Abbeel (2015)

[`REVIEW`](./reviews/TRPO.md) 	|	[`PAPER`](https://arxiv.org/pdf/1502.05477.pdf)

**(6) GAE**

[High-Dimensional Continuous Control Using Generalized Advantage Estimation]

John Schulman, Philipp Moritz, Sergey Levine, Michael I. Jordan and Pieter Abbeel(2016)

[`REVIEW`](./reviews/GAE.md) 	|	[`PAPER`](https://arxiv.org/pdf/1506.02438.pdf)

**(7) PPO**

[Proximal policy optimization algorithms]

John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov(2017)

[`REVIEW`](./reviews/PPO.md) 	|	[`PAPER`](https://arxiv.org/pdf/1707.06347.pdf)

**(8) TD3**

[Addressing Function Approximation Error in Actor-Critic Methods]

Scott Fujimoto , Herke van Hoof , David Meger (2018)

[`REVIEW`](./reviews/TD3.md)  	|	[`PAPER`](https://arxiv.org/pdf/1802.09477.pdf)	|	[`CODE`](https://github.com/CUN-bjy/gym-td3-keras)

**(9) SAC**

[Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor]

[`REVIEW`](./reviews/SAC.md) 	|	[`PAPER`](https://arxiv.org/pdf/1801.01290.pdf)

---

### Exploration

**(1) PER**

[Prioritized Experience Replay]

Tom Schaul, John Quan, Ioannis Antonoglou and David Silver, Google DeepMind(2015)

[`REVIEW`](./reviews/PER.md)	|	[`PAPER`](https://arxiv.org/pdf/1511.05952.pdf)

**(2) HER**

[Hindsight Experience Replay, Marcin Andrychowicz]

Marcin Andrychowicz∗ , Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel , Wojciech Zaremba ,OpenAI(2018)

`REVIEW`	|	[`PAPER`](https://arxiv.org/pdf/1707.01495.pdf)

---

### Reference

[Key Papers in Deep RL](https://spinningup.openai.com/en/latest/spinningup/keypapers.html#id106)

[PG Travel Guide](https://reinforcement-learning-kr.github.io/2018/06/29/0_pg-travel-guide/)

[utilForever/rl-paper-study](https://github.com/utilForever/rl-paper-study)

[Khanrc's blog](http://khanrc.github.io/)

rl-paper-review

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