darr/key_papers_in_deep_rl

key papers in deep rl

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.

content from:

https://spinningup.openai.com/en/latest/spinningup/keypapers.html

• Model-Free RL
• Exploration
• Transfer and Multitask RL
• Hierarchy
• Memory
• Model-Based RL
• Meta-RL
• Scaling RL
• RL in the Real World
• Safety
• Imitation Learning and Inverse Reinforcement Learning
• Reproducibility, Analysis, and Critique
• Bonus: Classic Papers in RL Theory or Review

1.Model-Free RL

a. Deep Q-Learning

• 1. Playing Atari with Deep Reinforcement Learning, Mnih et al, 2013. Algorithm: DQN. (origin address)
• 2. Deep Recurrent Q-Learning for Partially Observable MDPs, Hausknecht and Stone, 2015. Algorithm: Deep Recurrent Q-Learning. (origin address)
• 3. Dueling Network Architectures for Deep Reinforcement Learning, Wang et al, 2015. Algorithm: Dueling DQN. (origin address)
• 4. Deep Reinforcement Learning with Double Q-learning, Hasselt et al 2015. Algorithm: Double DQN. (origin address)
• 5. Prioritized Experience Replay, Schaul et al, 2015. Algorithm: Prioritized Experience Replay (PER). (origin address)
• 6. Rainbow: Combining Improvements in Deep Reinforcement Learning, Hessel et al, 2017. Algorithm: Rainbow DQN. (origin address)

b. Policy Gradients

• 7. Asynchronous Methods for Deep Reinforcement Learning, Mnih et al, 2016. Algorithm: A3C. (origin address)

• 8. Trust Region Policy Optimization, Schulman et al, 2015. Algorithm: TRPO. (origin address)

• 9. High-Dimensional Continuous Control Using Generalized Advantage Estimation, Schulman et al, 2015. Algorithm: GAE. (origin address)

• 10. Proximal Policy Optimization Algorithms, Schulman et al, 2017. Algorithm: PPO-Clip, PPO-Penalty. (origin address)

• 11. Emergence of Locomotion Behaviours in Rich Environments, Heess et al, 2017. Algorithm: PPO-Penalty. (origin address)

• 12. Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation, Wu et al, 2017. Algorithm: ACKTR. (origin address)

• 13. Sample Efficient Actor-Critic with Experience Replay, Wang et al, 2016. Algorithm: ACER. (origin address)

• 14. Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor, Haarnoja et al, 2018. Algorithm: SAC. (origin address)

c. Deterministic Policy Gradients

• 15. Deterministic Policy Gradient Algorithms, Silver et al, 2014. Algorithm: DPG. (origin address)
• 16. Continuous Control With Deep Reinforcement Learning, Lillicrap et al, 2015. Algorithm: DDPG. (origin address)
• 17. Addressing Function Approximation Error in Actor-Critic Methods, Fujimoto et al, 2018. Algorithm: TD3. (origin address)

d.Distributional RL

• 18. A Distributional Perspective on Reinforcement Learning, Bellemare et al, 2017. Algorithm: C51. (origin address)
• 19 Distributional Reinforcement Learning with Quantile Regression, Dabney et al, 2017. Algorithm: QR-DQN. (origin address)
• 20. Implicit Quantile Networks for Distributional Reinforcement Learning, Dabney et al, 2018. Algorithm: IQN. (origin address)
• 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. (origin address)

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. (origin address)
• 23. Action-depedent Control Variates for Policy Optimization via Stein’s Identity, Liu et al, 2017. Algorithm: Stein Control Variates. (origin address)
• 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. (origin address)

f. Path-Consistency Learning

• 25. Bridging the Gap Between Value and Policy Based Reinforcement Learning, Nachum et al, 2017. Algorithm: PCL. (origin address)
• 26. Trust-PCL: An Off-Policy Trust Region Method for Continuous Control, Nachum et al, 2017. Algorithm: Trust-PCL. (origin address)

g. Other Directions for Combining Policy-Learning and Q-learning

• 27. Combining Policy Gradient and Q-learning, O’Donoghue et al, 2016. Algorithm: PGQL. (origin address)
• 28. The Reactor: A Fast and Sample-Efficient Actor-Critic Agent for Reinforcement Learning, Gruslys et al, 2017. Algorithm: Reactor. (origin address)
• 29. Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning, Gu et al, 2017. Algorithm: IPG. (origin address)
• 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. (origin address)

h. Evolutionary Algorithms

• 31. Evolution Strategies as a Scalable Alternative to Reinforcement Learning, Salimans et al, 2017. Algorithm: ES. (origin address)

2. Exploration

a. Intrinsic Motivation

• 32. VIME: Variational Information Maximizing Exploration, Houthooft et al, 2016. Algorithm: VIME. (origin address)

• 33. Unifying Count-Based Exploration and Intrinsic Motivation, Bellemare et al, 2016. Algorithm: CTS-based Pseudocounts. (origin address)

• 34. Count-Based Exploration with Neural Density Models, Ostrovski et al, 2017. Algorithm: PixelCNN-based Pseudocounts. (origin address)

• 35. #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning, Tang et al, 2016. Algorithm: Hash-based Counts. (origin address)

• 36. EX2: Exploration with Exemplar Models for Deep Reinforcement Learning, Fu et al, 2017. Algorithm: EX2. (origin address)

• 37. Curiosity-driven Exploration by Self-supervised Prediction, Pathak et al, 2017. Algorithm: Intrinsic Curiosity Module (ICM). (origin address)

• 38. Large-Scale Study of Curiosity-Driven Learning, Burda et al, 2018. Contribution: Systematic analysis of how surprisal-based intrinsic motivation performs in a wide variety of environments. (origin address)

• 39. Exploration by Random Network Distillation, Burda et al, 2018. Algorithm: RND. (origin address)

b. Unsupervised RL

• 40. Variational Intrinsic Control, Gregor et al, 2016. Algorithm: VIC. (origin address)
• 41. Diversity is All You Need: Learning Skills without a Reward Function, Eysenbach et al, 2018. Algorithm: DIAYN. (origin address)
• 42. Variational Option Discovery Algorithms, Achiam et al, 2018. Algorithm: VALOR. (origin address)

3. Transfer and Multitask RL

• 43. Progressive Neural Networks, Rusu et al, 2016. Algorithm: Progressive Networks. (origin address)
• 44. Universal Value Function Approximators, Schaul et al, 2015. Algorithm: UVFA. (origin address)
• 45. Reinforcement Learning with Unsupervised Auxiliary Tasks, Jaderberg et al, 2016. Algorithm: UNREAL. (origin address)
• 46. The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously, Cabi et al, 2017. Algorithm: IU Agent. (origin address)
• 47. PathNet: Evolution Channels Gradient Descent in Super Neural Networks, Fernando et al, 2017. Algorithm: PathNet. (origin address)
• 48. Mutual Alignment Transfer Learning, Wulfmeier et al, 2017. Algorithm: MATL. (origin address)
• 49. Learning an Embedding Space for Transferable Robot Skills, Hausman et al, 2018. (origin address)
• 50. Hindsight Experience Replay, Andrychowicz et al, 2017. Algorithm: Hindsight Experience Replay (HER). (origin address)

4. Hierarchy

• 51. Strategic Attentive Writer for Learning Macro-Actions, Vezhnevets et al, 2016. Algorithm: STRAW. (origin address)
• 52. FeUdal Networks for Hierarchical Reinforcement Learning, Vezhnevets et al, 2017. Algorithm: Feudal Networks (origin address)
• 53. Data-Efficient Hierarchical Reinforcement Learning, Nachum et al, 2018. Algorithm: HIRO. (origin address)

5. Memory

• 54. Model-Free Episodic Control, Blundell et al, 2016. Algorithm: MFEC. (origin address)
• 55. Neural Episodic Control, Pritzel et al, 2017. Algorithm: NEC. (origin address)
• 56. Neural Map: Structured Memory for Deep Reinforcement Learning, Parisotto and Salakhutdinov, 2017. Algorithm: Neural Map. (origin address)
• 57. Unsupervised Predictive Memory in a Goal-Directed Agent, Wayne et al, 2018. Algorithm: MERLIN. (origin address)
• 58. Relational Recurrent Neural Networks, Santoro et al, 2018. Algorithm: RMC. (origin address)

6. Model-Based RL

a. Model is Learned

• 59. Imagination-Augmented Agents for Deep Reinforcement Learning, Weber et al, 2017. Algorithm: I2A. (origin address)
• 60. Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning, Nagabandi et al, 2017. Algorithm: MBMF. (origin address)
• 61. Model-Based Value Expansion for Efficient Model-Free Reinforcement Learning, Feinberg et al, 2018. Algorithm: MVE. (origin address)
• 62. Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion, Buckman et al, 2018. Algorithm: STEVE. (origin address)
• 63. Model-Ensemble Trust-Region Policy Optimization, Kurutach et al, 2018. Algorithm: ME-TRPO. (origin address)
• 64. Model-Based Reinforcement Learning via Meta-Policy Optimization, Clavera et al, 2018. Algorithm: MB-MPO. (origin address)
• 65. Recurrent World Models Facilitate Policy Evolution, Ha and Schmidhuber, 2018. (origin address)

b. Model is Given

• [66] Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm, Silver et al, 2017. Algorithm: AlphaZero.
• [67] Thinking Fast and Slow with Deep Learning and Tree Search, Anthony et al, 2017. Algorithm: ExIt.

7. Meta-RL

• [68] RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning, Duan et al, 2016. Algorithm: RL^2.
• [69] Learning to Reinforcement Learn, Wang et al, 2016.
• [70] Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, Finn et al, 2017. Algorithm: MAML.
• [71] A Simple Neural Attentive Meta-Learner, Mishra et al, 2018. Algorithm: SNAIL.

8. Scaling RL

• [72] Accelerated Methods for Deep Reinforcement Learning, Stooke and Abbeel, 2018. Contribution: Systematic analysis of parallelization in deep RL across algorithms.
• [73] IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures, Espeholt et al, 2018. Algorithm: IMPALA.
• [74] Distributed Prioritized Experience Replay, Horgan et al, 2018. Algorithm: Ape-X.
• [75] Recurrent Experience Replay in Distributed Reinforcement Learning, Anonymous, 2018. Algorithm: R2D2.
• [76] RLlib: Abstractions for Distributed Reinforcement Learning, Liang et al, 2017. Contribution: A scalable library of RL algorithm implementations. Documentation link.

9. RL in the Real World

• [77] Benchmarking Reinforcement Learning Algorithms on Real-World Robots, Mahmood et al, 2018.
• [78] Learning Dexterous In-Hand Manipulation, OpenAI, 2018.
• [79] QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation, Kalashnikov et al, 2018. Algorithm: QT-Opt.
• [80] Horizon: Facebook’s Open Source Applied Reinforcement Learning Platform, Gauci et al, 2018.

10. Safety

• [81] Concrete Problems in AI Safety, Amodei et al, 2016. Contribution: establishes a taxonomy of safety problems, serving as an important jumping-off point for future research. We need to solve these!
• [82] Deep Reinforcement Learning From Human Preferences, Christiano et al, 2017. Algorithm: LFP.
• [83] Constrained Policy Optimization, Achiam et al, 2017. Algorithm: CPO.
• [84] Safe Exploration in Continuous Action Spaces, Dalal et al, 2018. Algorithm: DDPG+Safety Layer.
• [85] Trial without Error: Towards Safe Reinforcement Learning via Human Intervention, Saunders et al, 2017. Algorithm: HIRL.
• [86] Leave No Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning, Eysenbach et al, 2017. Algorithm: Leave No Trace.

11. Imitation Learning and Inverse Reinforcement Learning

• [87] Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy, Ziebart 2010. Contributions: Crisp formulation of maximum entropy IRL.
• [88] Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization, Finn et al, 2016. Algorithm: GCL.
• [89] Generative Adversarial Imitation Learning, Ho and Ermon, 2016. Algorithm: GAIL.
• [90] DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills, Peng et al, 2018. Algorithm: DeepMimic.
• [91] Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow, Peng et al, 2018. Algorithm: VAIL.
• [92] One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL, Le Paine et al, 2018. Algorithm: MetaMimic.

12. Reproducibility, Analysis, and Critique

• [93] Benchmarking Deep Reinforcement Learning for Continuous Control, Duan et al, 2016. Contribution: rllab.
• [94] Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control, Islam et al, 2017.
• [95] Deep Reinforcement Learning that Matters, Henderson et al, 2017.
• [96] Where Did My Optimum Go?: An Empirical Analysis of Gradient Descent Optimization in Policy Gradient Methods, Henderson et al, 2018.
• [97] Are Deep Policy Gradient Algorithms Truly Policy Gradient Algorithms?, Ilyas et al, 2018.
• [98] Simple Random Search Provides a Competitive Approach to Reinforcement Learning, Mania et al, 2018.
• [99] Benchmarking Model-Based Reinforcement Learning, Wang et al, 2019.

13. Bonus:Classic Papers in RL Theory or Review

• [100] Policy Gradient Methods for Reinforcement Learning with Function Approximation, Sutton et al, 2000. Contributions: Established policy gradient theorem and showed convergence of policy gradient algorithm for arbitrary policy classes.
• [101] An Analysis of Temporal-Difference Learning with Function Approximation, Tsitsiklis and Van Roy, 1997. Contributions: Variety of convergence results and counter-examples for value-learning methods in RL.
• [102] Reinforcement Learning of Motor Skills with Policy Gradients, Peters and Schaal, 2008. Contributions: Thorough review of policy gradient methods at the time, many of which are still serviceable descriptions of deep RL methods.
• [103] Approximately Optimal Approximate Reinforcement Learning, Kakade and Langford, 2002. Contributions: Early roots for monotonic improvement theory, later leading to theoretical justification for TRPO and other algorithms.
• [104] A Natural Policy Gradient, Kakade, 2002. Contributions: Brought natural gradients into RL, later leading to TRPO, ACKTR, and several other methods in deep RL.
• [105] Algorithms for Reinforcement Learning, Szepesvari, 2009. Contributions: Unbeatable reference on RL before deep RL, containing foundations and theoretical background.