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 ¶

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.