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Key Papers in Deep Reinforcement Learning
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Key Papers in Deep RL
What follows is a list of papers in deep RL that are worth reading. This is far from comprehensive, but should provide a useful starting point for someone looking to do research in the field.
Table of Contents
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Key Papers in Deep RL
- 3. Transfer and Multitask RL
- 9. RL in the Real World
- 11. Imitation Learning and Inverse Reinforcement Learning
- 12. Bonus: Classic Papers in RL Theory or Review
a. Deep Q-Learning
[1] Playing Atari with Deep Reinforcement Learning , Mnih et al, 2013. Algorithm: DQN. [2] Deep Recurrent Q-Learning for Partially Observable MDPs , Hausknecht and Stone, 2015. Algorithm: Deep Recurrent Q-Learning. [3] Dueling Network Architectures for Deep Reinforcement Learning , Wang et al, 2015. Algorithm: Dueling DQN. [4] Deep Reinforcement Learning with Double Q-learning , Hasselt et al 2015. Algorithm: Double DQN. [5] Prioritized Experience Replay , Schaul et al, 2015. Algorithm: Prioritized Experience Replay (PER). [6] Rainbow: Combining Improvements in Deep Reinforcement Learning , Hessel et al, 2017. Algorithm: Rainbow DQN.b. Policy Gradients
[7] Asynchronous Methods for Deep Reinforcement Learning , Mnih et al, 2016. Algorithm: A3C. [8] Trust Region Policy Optimization , Schulman et al, 2015. Algorithm: TRPO. [9] High-Dimensional Continuous Control Using Generalized Advantage Estimation , Schulman et al, 2015. Algorithm: GAE. [10] Proximal Policy Optimization Algorithms , Schulman et al, 2017. Algorithm: PPO-Clip, PPO-Penalty. [11] Emergence of Locomotion Behaviours in Rich Environments , Heess et al, 2017. Algorithm: PPO-Penalty. [12] Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation , Wu et al, 2017. Algorithm: ACKTR. [13] Sample Efficient Actor-Critic with Experience Replay , Wang et al, 2016. Algorithm: ACER. [14] Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor , Haarnoja et al, 2018. Algorithm: SAC.c. Deterministic Policy Gradients
[15] Deterministic Policy Gradient Algorithms , Silver et al, 2014. Algorithm: DPG. [16] Continuous Control With Deep Reinforcement Learning , Lillicrap et al, 2015. Algorithm: DDPG. [17] Addressing Function Approximation Error in Actor-Critic Methods , Fujimoto et al, 2018. Algorithm: TD3.d. Distributional RL
[18] A Distributional Perspective on Reinforcement Learning , Bellemare et al, 2017. Algorithm: C51. [19] Distributional Reinforcement Learning with Quantile Regression , Dabney et al, 2017. Algorithm: QR-DQN. [20] Implicit Quantile Networks for Distributional Reinforcement Learning , Dabney et al, 2018. Algorithm: IQN. [21] Dopamine: A Research Framework for Deep Reinforcement Learning , Anonymous, 2018. Contribution: Introduces Dopamine, a code repository containing implementations of DQN, C51, IQN, and Rainbow. Code link.e. Policy Gradients with Action-Dependent Baselines
[22] Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic , Gu et al, 2016. Algorithm: Q-Prop. [23] Action-depedent Control Variates for Policy Optimization via Stein’s Identity , Liu et al, 2017. Algorithm: Stein Control Variates. [24] The Mirage of Action-Dependent Baselines in Reinforcement Learning , Tucker et al, 2018. Contribution: interestingly, critiques and reevaluates claims from earlier papers (including Q-Prop and stein control variates) and finds important methodological errors in them.f. Path-Consistency Learning
[25] Bridging the Gap Between Value and Policy Based Reinforcement Learning , Nachum et al, 2017. Algorithm: PCL. [26] Trust-PCL: An Off-Policy Trust Region Method for Continuous Control , Nachum et al, 2017. Algorithm: Trust-PCL.g. Other Directions for Combining Policy-Learning and Q-Learning
[27] Combining Policy Gradient and Q-learning , O’Donoghue et al, 2016. Algorithm: PGQL. [28] The Reactor: A Fast and Sample-Efficient Actor-Critic Agent for Reinforcement Learning , Gruslys et al, 2017. Algorithm: Reactor. [29] Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning , Gu et al, 2017. Algorithm: IPG. [30] Equivalence Between Policy Gradients and Soft Q-Learning , Schulman et al, 2017. Contribution: Reveals a theoretical link between these two families of RL algorithms.g. Evolutionary Algorithms
[31] Evolution Strategies as a Scalable Alternative to Reinforcement Learning , Salimans et al, 2017. Algorithm: ES.Recommend
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