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深度强化学习的核心论文

以下是深度强化中值得阅读的论文列表。这距离全面还 远,但是应该为希望在该领域进行研究的人提供有用的起点。

1. 免模型强化学习

a. 深度 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. 策略梯度

[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. 确定性策略梯度

[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. 分布式强化学习

[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. 带有 Action-Dependent Baselines 的策略梯度

[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. 路径一致性学习(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. 其他结合策略梯度和Q-Learning的方向

[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. 进化算法

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

2. 探索

a. 内在激励(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. 非监督强化学习

[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. 迁移和多任务强化学习

[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. 层次(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. 记忆(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. 有模型强化学习

a. 模型可被学习

[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. 模型已知

[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. 元学习(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. 扩展强化学习

[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. 现实世界的强化学习

[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. 安全性

[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. 模仿学习和逆强化学习

[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. 可复现、分析和评价

[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. 额外奖励:强化学习理论的经典论文

[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.