RL 真经

2018 年 12 月 28 日 CreateAMind
RL 真经

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



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

  • Key Papers in Deep RL

    • 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.
[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.

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.


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深度强化学习 (DRL) 是一种使用深度学习技术扩展传统强化学习方法的一种机器学习方法。 传统强化学习方法的主要任务是使得主体根据从环境中获得的奖赏能够学习到最大化奖赏的行为。然而,传统无模型强化学习方法需要使用函数逼近技术使得主体能够学习出值函数或者策略。在这种情况下,深度学习强大的函数逼近能力自然成为了替代人工指定特征的最好手段并为性能更好的端到端学习的实现提供了可能。

Many reinforcement-learning researchers treat the reward function as a part of the environment, meaning that the agent can only know the reward of a state if it encounters that state in a trial run. However, we argue that this is an unnecessary limitation and instead, the reward function should be provided to the learning algorithm. The advantage is that the algorithm can then use the reward function to check the reward for states that the agent hasn't even encountered yet. In addition, the algorithm can simultaneously learn policies for multiple reward functions. For each state, the algorithm would calculate the reward using each of the reward functions and add the rewards to its experience replay dataset. The Hindsight Experience Replay algorithm developed by Andrychowicz et al. (2017) does just this, and learns to generalize across a distribution of sparse, goal-based rewards. We extend this algorithm to linearly-weighted, multi-objective rewards and learn a single policy that can generalize across all linear combinations of the multi-objective reward. Whereas other multi-objective algorithms teach the Q-function to generalize across the reward weights, our algorithm enables the policy to generalize, and can thus be used with continuous actions.

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