XuanPolicy/original_papers

• Deep RL Paper list
• • Policy Gradient
  • Value Based
  • Distributed Training
  • Empirical Study about What Matters in Deep RL
  • Benchmarking XXX in RL
  • Reinforcement Learning Libraries

Deep RL Paper list

Policy Gradient

• Vanilla Policy Gradient(VPG), Richard S. Sutton, et al., 1992
• Natural Policy Gradient(NPG), Sham Kakade. 2002
• Parallel Advantage Actor Critic(PAAC), Alfredo V. Clemente, et al., 2017
• Actor Critic using Kronecker-Factored Trust Region(ACKTR), Yuhuai Wu, 2017
• Actor Critic with Experience Replay(ACER), Ziyu Wang, 2017
• Trust Region Policy Optimization(TRPO), John Schulman, et al. 2015
• Proximal Policy Optimization Methods(PPO), John Schulman, et al. 2017
• Distributed Proximal Policy Optimization(DPPO), Nicolas Heess, et al. 2017
• Deep Deterministic Policy Gradient(DDPG), Timothy P. Lillicrap, et al. 2015
• Twin-Delayed DDPG(TD3), Scott Fujimoto, et al. 2018
• Soft Actor Critic(SAC), Tuomas Haarnoja, et al. 2018
• Phasic Policy Gradient(PPG), Karl Cobbe, et al. 2020

Value Based

• Nature DQN, Volodymyr Mnih, et al., 2015
• Double DQN, Hado van Hasselt, et al., 2015
• Dueling DQN, Ziyu Wang, et al., 2015
• NoisyNet DQN, Meire Fortunato, et al., 2017
• Prioritized Replay Buffer DQN, Tom Schaul, et al. 2015
• Distributional DQN, Marc G. Bellemare, et al. 2017
• Rainbow DQN, Matteo Hessel, et al. 2017

Distributed Training

• Asynchronous Advantage Actor-Critic(A3C), Volodymyr Mnih, et al., 2016
• Ape-X, Dan Horgan, et al., 2018
• Distributed Distributional DDPG(D4PG), Gabriel Barth-Maron, et al. 2018
• Importance Weighted Actor-Learner Architecture(IMPALA), Lasse Espeholt, et al. 2018
• Decentralized Distributed Proximal Policy Optimization, Erik Wijmans, et al. 2019
• Scalable and Efficient Deep-RL(SEED RL), Lasse Espeholt, et al. 2019

Empirical Study about What Matters in Deep RL

• Deep Reinforcement Learning that Matters, Peter Henderson, et al. 2017
• Implementation Matters In Deep Policy Gradients: A Case Study On PPO and TRPO, Logan Engstrom, et al. 2019
• What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study, Marcin Andrychowicz, et al. 2020
• The Successful Ingredients of Policy Gradient Algorithms, Sven Gronauer, et al. 2021
• Regularzation Matters in Policy Optimization - An Empirical Study On Continuous Control, Zhuang Liu, et al. 2021

Benchmarking XXX in RL

• Benchmarking Deep Reinforcement Learning for Continuous Control, Yan Duan, et al. 2016
• Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks, Georgios Papoudakis, et al. 2021
• Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms, Chao Yu, et al. 2021
• Benchmarking Model-Based Reinforcement Learning, Tingwu Wang, et al. 2020
• Benchmarking Reinforcement Learning Algorithms on Real-World Robots, A. Rupam Mahmood, et al. 2018
• Benchmarking Safe Exploration in Deep Reinforcement Learning. Alex Ray, et al. 2019
• Benchmarking Batch Deep Reinforcement Learning Algorithms. Scott Fujimoto, et al. 2019
• Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment. Adrien Ali Taïga, et al. 2019
• Benchmarking Unsupervised Representation Learning for Continuous Control, Rika Antonova, et al. 2020

Reinforcement Learning Libraries

• ChainerRL: A Deep Reinforcement Learning Library, Yasuhiro Fujita, et al. 2021
• Tonic: A Deep Reinforcement Learning Library for Fast Prototyping and Benchmarking, Fabio Pardo, 2021
• MushroomRL: Simplifying Reinforcement Learning Research, Carlo D’Eramo, 2020
• rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch, Adam Stooke, et al. 2019
• Efficient Reinforcement Learning Development with RLZoo. Zihan Ding, et al. 2021
• Tianshou: A Highly Modularized Deep Reinforcement Learning Library, Jiayi Weng, et al. 2021
• Dopamine: A Research Framework For Deep Reinforcement Learning. Pablo Samuel Castro, et al. 2018
• RLlib: Abstractions for Distributed Reinforcement Learning. Eric Liang, et al. 2018