rl-paper-review

Reinforcement Learning Roadmap

GITMIND < Link!!

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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 | PAPER

(2) DPG

[Deterministic policy gradient algorithms]

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

REVIEW | PAPER

(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 | PAPER | CODE

(4) NPG

[A natural policy gradient]

Sham Kakade(2002)

REVIEW | PAPER

(5) TRPO

[Trust region policy optimization]

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

REVIEW | PAPER

(6) GAE

[High-Dimensional Continuous Control Using Generalized Advantage Estimation]

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

REVIEW | PAPER

(7) PPO

[Proximal policy optimization algorithms]

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

REVIEW | PAPER

(8) TD3

[Addressing Function Approximation Error in Actor-Critic Methods]

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

REVIEW | PAPER | CODE

(9) SAC

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

REVIEW | PAPER

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Exploration

(1) PER

[Prioritized Experience Replay]

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

REVIEW | PAPER

(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

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Reference

Key Papers in Deep RL

PG Travel Guide

utilForever/rl-paper-study

Khanrc's blog