OpenAI官方发布:强化学习中的关键论文

2018 年 12 月 12 日 专知

【导读】OpenAI 在教学资源合集 Spinning Up中发布了强化学习中的关键论文,列举了强化学习不同领域的代表性文章来指导研究者的学习。此外Spinning Up 包含清晰的 RL 代码示例、习题、文档和教程可供参考。


1. Model-Free RL

2. Exploration

3. Transfer and Multitask RL

4. Hierarchy

5. Memory

6. Model-Based RL

7. Meta-RL

8. Scaling RL

9. RL in the Real World

10. Safety

11. Imitation Learning and Inverse Reinforcement Learning

12. Reproducibility, Analysis, and Critique

13. 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.

    https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf

[2] Deep Recurrent Q-Learning for Partially Observable MDPs, Hausknecht and Stone, 2015. Algorithm: Deep Recurrent Q-Learning.

    https://arxiv.org/abs/1507.06527

[3] Dueling Network Architectures for Deep Reinforcement Learning, Wang et al, 2015. Algorithm: Dueling DQN.

    https://arxiv.org/abs/1511.06581

[4] Deep Reinforcement Learning with Double Q-learning, Hasselt et al 2015. Algorithm: Double DQN.

    https://arxiv.org/abs/1509.06461

[5] Prioritized Experience Replay, Schaul et al, 2015. Algorithm: Prioritized Experience Replay (PER).

    https://arxiv.org/abs/1511.05952

[6] Rainbow: Combining Improvements in Deep Reinforcement Learning, Hessel et al, 2017. Algorithm: Rainbow DQN.

    https://arxiv.org/abs/1710.02298


b. Policy Gradients

[7] Asynchronous Methods for Deep Reinforcement Learning, Mnih et al, 2016. Algorithm: A3C.

    https://arxiv.org/abs/1602.01783

[8] Trust Region Policy Optimization, Schulman et al, 2015. Algorithm: TRPO.

    https://arxiv.org/abs/1502.05477

[9] High-Dimensional Continuous Control Using Generalized Advantage Estimation, Schulman et al, 2015. Algorithm: GAE.

    https://arxiv.org/abs/1506.02438

[10] Proximal Policy Optimization Algorithms, Schulman et al, 2017. Algorithm: PPO-Clip, PPO-Penalty.

    https://arxiv.org/abs/1707.06347

[11] Emergence of Locomotion Behaviours in Rich Environments, Heess et al, 2017. Algorithm: PPO-Penalty.

    https://arxiv.org/abs/1707.02286

[12] Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation, Wu et al, 2017. Algorithm: ACKTR.

    https://arxiv.org/abs/1708.05144

[13] Sample Efficient Actor-Critic with Experience Replay, Wang et al, 2016. Algorithm: ACER.

    https://arxiv.org/abs/1611.01224

[14] Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor, Haarnoja et al, 2018. Algorithm: SAC.

    https://arxiv.org/abs/1801.01290


c. Deterministic Policy Gradients

[15] Deterministic Policy Gradient Algorithms, Silver et al, 2014. Algorithm: DPG.

    http://proceedings.mlr.press/v32/silver14.pdf


[16] Continuous Control With Deep Reinforcement Learning, Lillicrap et al, 2015. Algorithm: DDPG.

    https://arxiv.org/abs/1509.02971


[17] Addressing Function Approximation Error in Actor-Critic Methods, Fujimoto et al, 2018. Algorithm: TD3.

    https://arxiv.org/abs/1802.09477


d. Distributional RL

[18] A Distributional Perspective on Reinforcement Learning, Bellemare et al, 2017. Algorithm: C51.

    https://arxiv.org/abs/1707.06887


[19] Distributional Reinforcement Learning with Quantile Regression, Dabney et al, 2017. Algorithm: QR-DQN.

    https://arxiv.org/abs/1710.10044


[20] Implicit Quantile Networks for Distributional Reinforcement Learning, Dabney et al, 2018. Algorithm: IQN.

    https://arxiv.org/abs/1806.06923


[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.

    https://openreview.net/forum?id=ByG_3s09KX


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.


13. Bonus: Classic Papers in RL Theory or Review

[99] 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.


[100] 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.


[101] 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.


[102] 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.


[103] A Natural Policy Gradient, Kakade, 2002. Contributions: Brought natural gradients into RL, later leading to TRPO, ACKTR, and several other methods in deep RL.


[104] Algorithms for Reinforcement Learning, Szepesvari, 2009. Contributions: Unbeatable reference on RL before deep RL, containing foundations and theoretical background.


参考链接:

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

-END-

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强化学习(RL)是机器学习的一个领域,与软件代理应如何在环境中采取行动以最大化累积奖励的概念有关。除了监督学习和非监督学习外,强化学习是三种基本的机器学习范式之一。 强化学习与监督学习的不同之处在于,不需要呈现带标签的输入/输出对,也不需要显式纠正次优动作。相反,重点是在探索(未知领域)和利用(当前知识)之间找到平衡。 该环境通常以马尔可夫决策过程(MDP)的形式陈述,因为针对这种情况的许多强化学习算法都使用动态编程技术。经典动态规划方法和强化学习算法之间的主要区别在于,后者不假设MDP的确切数学模型,并且针对无法采用精确方法的大型MDP。

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