강화 학습 필수 논문 목록을 openai에서 뽑아봤다.

1. Model-Free RL

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.

    h. Evolutionary Algorithms

  • [31] Evolution Strategies as a Scalable Alternative to Reinforcement Learning, Salimans et al, 2017. Algorithm: ES.

2. Exploration

a. Intrinsic Motivation

  • [32] VIME: Variational Information Maximizing Exploration, Houthooft et al, 2016. Algorithm: VIME.
  • [33] Unifying Count-Based Exploration and Intrinsic Motivation, Bellemare et al, 2016. Algorithm: CTS-based Pseudocounts.
  • [34] Count-Based Exploration with Neural Density Models, Ostrovski et al, 2017. Algorithm: PixelCNN-based Pseudocounts.
  • [35] Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning, Tang et al, 2016. Algorithm: Hash-based Counts.
  • [36] EX2: Exploration with Exemplar Models for Deep Reinforcement Learning, Fu et al, 2017. Algorithm: EX2.
  • [37] Curiosity-driven Exploration by Self-supervised Prediction, Pathak et al, 2017. Algorithm: Intrinsic Curiosity Module (ICM).
  • [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.
  • [39] Exploration by Random Network Distillation, Burda et al, 2018. Algorithm: RND.

    b. Unsupervised RL

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

3. Transfer and Multitask RL

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

4. Hierarchy

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

5. Memory

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

6. Model-Based RL

a. Model is Learned

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

    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.

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