深度强化学习- 最全深度强化学习资料

其他 2020-02-20 11:36:31 阅读次数: 0

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本文链接： https://blog.csdn.net/gsww404/article/details/103074046

本工作是一项由深度强化学习实验室(Deep Reinforcement Learning Laboratory, DeepRL-Lab)发起的项目。
文章同步于Github仓库：
https://github.com/NeuronDance/DeepRL/tree/master/A-Guide-Resource-For-DeepRL(点击进入GitHub)
欢迎大家Star, Fork和Contribution.

Contents

1. Books

  1. Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto (2017),Chinese-Edtion, Code
  2. Algorithms for Reinforcement Learning by Csaba Szepesvari (updated 2019)
  3. Deep Reinforcement Learning Hands-On by Maxim Lapan (2018),Code
  4. Reinforcement learning, State-Of-The- Art by Marco Wiering, Martijin van Otterlo
  5. Deep Reinforcement Learning in Action by Alexander Zai and Brandon Brown (in progress)
  6. Grokking Deep Reinforcement Learning by Miguel Morales (in progress)
  7. Multi-Agent Machine Learning A Reinforcement Approach【百度云链接】 by Howard M.Schwartz(2017)
  8. 强化学习在阿里的技术演进与业务创新 by Alibaba Group
  9. Hands-On Reinforcement Learning with Python(百度云链接)
  10. Reinforcement Learning And Optimal Control by Dimitri P. Bertsekas, 2019

2. Courses

  1. UCL Course on RL(★★★) by David Sliver, Video-en,Video-zh

  2. OpenAI’s Spinning Up in Deep RL by OpenAI(2018)

  3. Udacity-Deep Reinforcement learning, 2019-10-31

  4. Stanford CS-234: Reinforcement Learning (2019), Videos

  5. DeepMind Advanced Deep Learning & Reinforcement Learning (2018),Videos

  6. GeorgiaTech CS-8803 Deep Reinforcement Learning (2018?)

  7. UC Berkeley CS294-112 Deep Reinforcement Learning (2018 Fall),Video-zh

  8. Deep RL Bootcamp by Berkeley CA(2017)

  9. Thomas Simonini’s Deep Reinforcement Learning Course

  10. CS-6101 Deep Reinforcement Learning , NUS SoC, 2018/2019, Semester II

  11. Course on Reinforcement Learning by Alessandro Lazaric，2018

  12. Learn Deep Reinforcement Learning in 60 days

3. Survey-and-Frontier

  1. Deep Reinforcement Learning by Yuxi Li

  2. Algorithms for Reinforcement Learning by Morgan & Claypool, 2009

  3. Modern Deep Reinforcement Learning Algorithms by Sergey Ivanov(54-Page)

  4. Deep Reinforcement Learning: An Overview (2018)

  5. A Brief Survey of Deep Reinforcement Learning (2017)

  6. Deep Reinforcement Learning Doesn’t Work Yet（★） by Irpan, Alex(2018), ChineseVersion

  7. Deep Reinforcement Learning that Matters(★) by Peter Henderson1, Riashat Islam1

  8. A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress

  9. Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

  10. An Introduction to Deep Reinforcement Learning

  11. Challenges of Real-World Reinforcement Learning

  12. Topics in Reinforcement Learning

  13. Reinforcement Learning: A Survey,1996.

  14. A Tutorial Survey of Reinforcement Learning, Sadhana,1994.

  15. Reinforcement Learning in Robotics, A Survey, 2013

  16. A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation., 2018

  17. Universal Reinforcement Learning Algorithms: Survey and Experiments,2017

  18. Bayesian Reinforcement Learning: A Survey, 2016

  19. Benchmarking Reinforcement Learning Algorithms on Real-World Robots

4. Environment-and-Framework

  • OpenAI Gym (GitHub) (docs)

  • rllab (GitHub) (readthedocs)

  • Ray (Doc)

  • Dopamine: https://github.com/google/dopamine (uses some tensorflow)

  • trfl: https://github.com/deepmind/trfl (uses tensorflow)

  • ChainerRL (GitHub) (API: Python)

  • Surreal GitHub (API: Python) (support: Stanford Vision and Learning Lab).Paper

  • PyMARL GitHub (support: http://whirl.cs.ox.ac.uk/)

  • TF-Agents: https://github.com/tensorflow/agents (uses tensorflow)

  • TensorForce (GitHub) (uses tensorflow)

  • RL-Glue (Google Code Archive) (API: C/C++, Java, Matlab, Python, Lisp) (support: Alberta)

  • MAgent https://github.com/geek-ai/MAgent (uses tensorflow)

  • RLlib http://ray.readthedocs.io/en/latest/rllib.html (API: Python)

  • http://burlap.cs.brown.edu/ (API: Java)

  • rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch

  • robotics-rl-srl - S-RL Toolbox: Reinforcement Learning (RL) and State Representation Learning (SRL) for Robotics

  • pysc2: StarCraft II Learning Environment

  • Arcade-Learning-Environment

  • OpenAI universe - A software platform for measuring and training an AI’s general intelligence across the world’s supply of games, websites and other applications

  • DeepMind Lab - A customisable 3D platform for agent-based AI research

  • Project Malmo - A platform for Artificial Intelligence experimentation and research built on top of Minecraft by Microsoft

  • Retro Learning Environment - An AI platform for reinforcement learning based on video game emulators. Currently supports SNES and Sega Genesis. Compatible with OpenAI gym.

  • torch-twrl - A package that enables reinforcement learning in Torch by Twitter

  • UETorch - A Torch plugin for Unreal Engine 4 by Facebook

  • TorchCraft - Connecting Torch to StarCraft

  • rllab - A framework for developing and evaluating reinforcement learning algorithms, fully compatible with OpenAI Gym

  • TensorForce - Practical deep reinforcement learning on TensorFlow with Gitter support and OpenAI Gym/Universe/DeepMind Lab integration.

  • OpenAI lab - An experimentation system for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras.

  • keras-rl - State-of-the art deep reinforcement learning algorithms in Keras designed for compatibility with OpenAI.

  • BURLAP - Brown-UMBC Reinforcement Learning and Planning, a library written in Java

  • MAgent - A Platform for Many-agent Reinforcement Learning.

  • Ray RLlib - Ray RLlib is a reinforcement learning library that aims to provide both performance and composability.

  • SLM Lab - A research framework for Deep Reinforcement Learning using Unity, OpenAI Gym, PyTorch, Tensorflow.

  • Unity ML Agents - Create reinforcement learning environments using the Unity Editor

  • Intel Coach - Coach is a python reinforcement learning research framework containing implementation of many state-of-the-art algorithms.

  • ELF - An End-To-End, Lightweight and Flexible Platform for Game Research

  • Unity ML-Agents Toolkit

  • rlkit

  • https://gym.openai.com/envs/#classic_control

  • https://github.com/erlerobot/gym-gazebo

  • https://github.com/robotology/gym-ignition

  • https://github.com/dartsim/gym-dart

  • https://github.com/Roboy/gym-roboy

  • https://github.com/openai/retro

  • https://github.com/openai/gym-soccer

  • https://github.com/duckietown/gym-duckietown

  • https://github.com/Unity-Technologies/ml-agents (Unity, multiagent)

  • https://github.com/koulanurag/ma-gym (multiagent)

  • https://github.com/ucuapps/modelicagym

  • https://github.com/mwydmuch/ViZDoom

  • https://github.com/benelot/pybullet-gym

  • https://github.com/Healthcare-Robotics/assistive-gym

  • https://github.com/Microsoft/malmo

  • https://github.com/nadavbh12/Retro-Learning-Environment

  • https://github.com/twitter/torch-twrl

  • https://github.com/arex18/rocket-lander

  • https://github.com/ppaquette/gym-doom

  • https://github.com/thedimlebowski/Trading-Gym

  • https://github.com/Phylliade/awesome-openai-gym-environments

  • https://github.com/deepmind/pysc2 (by DeepMind) (Blizzard StarCraft II Learning Environment (SC2LE) component)

5. Baselines-and-Benchmarks

  1. https://github.com/openai/baselines 【stalbe-baseline】
  2. rl-baselines-zoo
  3. ROBEL (google-research/robel)
  4. RLBench (stepjam/RLBench)
  5. https://martin-thoma.com/sota/#reinforcment-learning
  6. https://github.com/rlworkgroup/garage
  7. Atari Environments Scores

6. Algorithms

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1. DQN serial
  1. Playing Atari with Deep Reinforcement Learning [arxiv] [code]
  2. Deep Reinforcement Learning with Double Q-learning [arxiv] [code]
  3. Dueling Network Architectures for Deep Reinforcement Learning [arxiv] [code]
  4. Prioritized Experience Replay [arxiv] [code]
  5. Noisy Networks for Exploration [arxiv] [code]
  6. A Distributional Perspective on Reinforcement Learning [arxiv] [code]
  7. Rainbow: Combining Improvements in Deep Reinforcement Learning [arxiv] [code]
2. Others

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Algorithm Codeing
  1. Deep-Reinforcement-Learning-Algorithms-with-PyTorch

#7. Applications

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7.1 Basic

  1. Reinforcement Learning Applications
  2. IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control by Hua Wei，Guanjie Zheng(2018)
  3. Deep Reinforcement Learning by Yuxi Li, 2018
  4. Deep Reinforcement Learning in Robotics

7.2 Robotics

  • Policy Gradient Reinforcement Learning for Fast Quadrupedal Locomotion (Kohl, ICRA 2004) [Paper]
  • Robot Motor SKill Coordination with EM-based Reinforcement Learning (Kormushev, IROS 2010) [Paper] [Video]
  • Generalized Model Learning for Reinforcement Learning on a Humanoid Robot (Hester, ICRA 2010) [Paper] [Video]
  • Autonomous Skill Acquisition on a Mobile Manipulator (Konidaris, AAAI 2011) [Paper] [Video]
  • PILCO: A Model-Based and Data-Efficient Approach to Policy Search (Deisenroth, ICML 2011) [Paper]
  • Incremental Semantically Grounded Learning from Demonstration (Niekum, RSS 2013) [Paper]
  • Efficient Reinforcement Learning for Robots using Informative Simulated Priors (Cutler, ICRA 2015) [Paper] [Video]
  • Robots that can adapt like animals (Cully, Nature 2015) [Paper] [Video] [Code]
  • Black-Box Data-efficient Policy Search for Robotics (Chatzilygeroudis, IROS 2017) [Paper] [Video] [Code]

#8. Advanced-Topics

8.1. Model-free RL

  1. playing atari with deep reinforcement learning NIPS Deep Learning Workshop 2013. paper

    Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller

  2. Human-level control through deep reinforcement learning Nature 2015. paper

    Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg & Demis Hassabis

  3. Deep Reinforcement Learning with Double Q-learning AAAI 16. paper

    Hado van Hasselt, Arthur Guez, David Silver

  4. Dueling Network Architectures for Deep Reinforcement Learning ICML16. paper

    Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas

  5. Deep Recurrent Q-Learning for Partially Observable MDPs AAA15. paper

    Matthew Hausknecht, Peter Stone

  6. Prioritized Experience Replay ICLR 2016. paper

    Tom Schaul, John Quan, Ioannis Antonoglou, David Silver

  7. Asynchronous Methods for Deep Reinforcement Learning ICML2016. paper

    Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu

  8. A Distributional Perspective on Reinforcement Learning ICML2017. paper

    Marc G. Bellemare, Will Dabney, Rémi Munos

  9. Noisy Networks for Exploration ICLR2018. paper

    Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Ian Osband, Alex Graves, Vlad Mnih, Remi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg

  10. Rainbow: Combining Improvements in Deep Reinforcement Learning AAAI2018. paper

    Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David Silver

8.2. Model-based RL

  1. Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion NIPS2018. paper

    Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee

  2. Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning ICML2018.paper

    Vladimir Feinberg, Alvin Wan, Ion Stoica, Michael I. Jordan, Joseph E. Gonzalez, Sergey Levine

  3. Value Prediction Network NIPS2017. paper

    Vladimir Feinberg, Alvin Wan, Ion Stoica, Michael I. Jordan, Joseph E. Gonzalez, Sergey Levine

  4. Imagination-Augmented Agents for Deep Reinforcement Learning NIPS2017. paper

    Théophane Weber, Sébastien Racanière, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adria Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter Battaglia, Demis Hassabis, David Silver, Daan Wierstra

  5. Continuous Deep Q-Learning with Model-based Acceleration ICML2016. paper

    Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, Sergey Levine

  6. Uncertainty-driven Imagination for Continuous Deep Reinforcement Learning CoRL2017. paper

    Gabriel Kalweit, Joschka Boedecker

  7. Model-Ensemble Trust-Region Policy Optimization ICLR2018. paper

    Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, Pieter Abbeel

  8. Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models NIPS2018. paper

    Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine

  9. Dyna, an integrated architecture for learning, planning, and reacting ACM1991. paper

    Sutton, Richard S

  10. Learning Continuous Control Policies by Stochastic Value Gradients NIPS 2015. paper

    Nicolas Heess, Greg Wayne, David Silver, Timothy Lillicrap, Yuval Tassa, Tom Erez

  11. Imagination-Augmented Agents for Deep Reinforcement Learning NIPS 2017. paper

    Théophane Weber, Sébastien Racanière, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adria Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter Battaglia, Demis Hassabis, David Silver, Daan Wierstra

  12. Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks ICLR 2017. paper

    Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft

8.3 Function Approximation methods (Least-Square Temporal Difference, Least-Square Policy Iteration)

  • Linear Least-Squares Algorithms for Temporal Difference Learning, Machine Learning, 1996. [Paper]
  • Model-Free Least Squares Policy Iteration, NIPS, 2001. [Paper] [Code]

8.4 Policy Search/Policy Gradient

  • Policy Gradient Methods for Reinforcement Learning with Function Approximation, NIPS, 1999. [Paper]
  • Natural Actor-Critic, ECML, 2005. [Paper]
  • Policy Search for Motor Primitives in Robotics, NIPS, 2009. [Paper]
  • Relative Entropy Policy Search, AAAI, 2010. [Paper]
  • Path Integral Policy Improvement with Covariance Matrix Adaptation, ICML, 2012. [Paper]
  • Policy Gradient Reinforcement Learning for Fast Quadrupedal Locomotion, ICRA, 2004. [Paper]
  • PILCO: A Model-Based and Data-Efficient Approach to Policy Search, ICML, 2011. [Paper]
  • Learning Dynamic Arm Motions for Postural Recovery, Humanoids, 2011. [Paper]
  • Black-Box Data-efficient Policy Search for Robotics, IROS, 2017. [Paper]

8.5 Hierarchical RL

  • Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning, Artificial Intelligence, 1999. [Paper]
  • Building Portable Options: Skill Transfer in Reinforcement Learning, IJCAI, 2007. [Paper]

8.6 Inverse RL

  1. updating…

8.7 Meta RL

  1. updating…

8.8. Rewards

  1. Deep Reinforcement Learning Models: Tips & Tricks for Writing Reward Functions
  2. Meta Reward Learning

8.9. Policy Gradient

  1. Policy Gradient

8.10. Distributed Reinforcement Learning

  1. Asynchronous Methods for Deep Reinforcement Learning by ICML 2016.paper
  2. GA3C: GPU-based A3C for Deep Reinforcement Learning by Iuri Frosio, Stephen Tyree, NIPS 2016
  3. Distributed Prioritized Experience Replay by Dan Horgan, John Quan, David Budden,ICLR 2018
  4. IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures by Lasse Espeholt, Hubert Soyer, Remi Munos ,ICML 2018
  5. Distributed Distributional Deterministic Policy Gradients by Gabriel Barth-Maron, Matthew W. Hoffman, ICLR 2018.
  6. Emergence of Locomotion Behaviours in Rich Environments by Nicolas Heess, Dhruva TB, Srinivasan Sriram, 2017
  7. GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning by Jacky Liang, Viktor Makoviychuk, 2018
  8. Recurrent Experience Replay in Distributed Reinforcement Learning bySteven Kapturowski, Georg Ostrovski, ICLR 2019.

#9. Relate-Coureses

9.1. Game Theory

  1. Game Theory Course, Yale University
  2. Game Theory - The Full Course, Stanford University
  3. Algorithmic Game Theory (CS364A, Fall 2013) , Stanford University

9.2. other

…

#10. Multi-Agents

10.1 Tutorial and Books

10.2 Review Papers

10.3 Framework papers

10.4 Joint action learning

10.5 Cooperation and competition

10.6 Coordination

10.7 Security

10.8 Self-Play

10.9 Learning To Communicate

10.10 Transfer Learning

10.11 Imitation and Inverse Reinforcement Learning

10.12 Meta Learning

10.13 Application

#11. Paper-Resources

2019-07

Jun

April-May

March 2019

Feb 2019

Jan 2019

2018

  • Accelerated Methods for Deep Reinforcement Learning. arxiv
  • A Deep Reinforcement Learning Chatbot (Short Version). arxiv
  • AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search. arxiv ⭐️
  • A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress. arxiv
  • Composable Deep Reinforcement Learning for Robotic Manipulation. arxiv
  • Cooperative Multi-Agent Reinforcement Learning for Low-Level Wireless Communication. arxiv
  • Deep Reinforcement Fuzzing. arxiv
  • Deep Reinforcement Learning of Cell Movement in the Early Stage of C. elegans Embryogenesis. arxiv
  • Deep Reinforcement Learning For Sequence to Sequence Models. arxiv code
  • Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods. arxiv
  • Deep Reinforcement Learning in Portfolio Management. arxiv code
  • Deep Reinforcement Learning using Capsules in Advanced Game Environments. arxiv
  • Deep Reinforcement Learning with Model Learning and Monte Carlo Tree Search in Minecraft. arxiv
  • Distributed Deep Reinforcement Learning: Learn how to play Atari games in 21 minutes. arxiv code
  • Diversity is All You Need: Learning Skills without a Reward Function. arxiv
  • Faster Deep Q-learning using Neural Episodic Control. arxiv
  • Feedback-Based Tree Search for Reinforcement Learning. arxiv
  • Feudal Reinforcement Learning for Dialogue Management in Large Domains. arxiv
  • Forward-Backward Reinforcement Learning. arxiv
  • Hierarchical Reinforcement Learning: Approximating Optimal Discounted TSP Using Local Policies. arxiv
  • IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures. arxiv
  • Kickstarting Deep Reinforcement Learning. arxiv
  • Learning a Prior over Intent via Meta-Inverse Reinforcement Learning. arxiv
  • Meta Reinforcement Learning with Latent Variable Gaussian Processes. arxiv
  • Multi-Agent Reinforcement Learning: A Report on Challenges and Approaches. arxiv
  • Pretraining Deep Actor-Critic Reinforcement Learning Algorithms With Expert Demonstrations. arxiv
  • Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents. arxiv
  • Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning. arxiv
  • Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review. arxiv
  • Reinforcement Learning from Imperfect Demonstrations. arxiv
  • Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application. arxiv
  • RUDDER: Return Decomposition for Delayed Rewards. arxiv code
  • Semi-parametric Topological Memory for Navigation. arxiv tensorflow
  • Shared Autonomy via Deep Reinforcement Learning. arxiv
  • Setting up a Reinforcement Learning Task with a Real-World Robot. arxiv
  • Simple random search provides a competitive approach to reinforcement learning. arxiv code
  • Unsupervised Meta-Learning for Reinforcement Learning. arxiv
  • Using reinforcement learning to learn how to play text-based games. arxiv

2017

  • A Deep Reinforcement Learning Chatbot. arxiv
  • A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem. arxiv code
  • A Deep Reinforced Model for Abstractive Summarization. arxiv
  • A Distributional Perspective on Reinforcement Learning. arxiv
  • A Laplacian Framework for Option Discovery in Reinforcement Learning. arxiv ⭐️
  • Boosting the Actor with Dual Critic. arxiv
  • Bridging the Gap Between Value and Policy Based Reinforcement Learning. arxiv
  • Car Racing using Reinforcement Learning. pdf
  • Cold-Start Reinforcement Learning with Softmax Policy Gradients. arxiv
  • Curiosity-driven Exploration by Self-supervised Prediction. arxiv tensorflow
  • Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning. arxiv code
  • DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning. arxiv code
  • Deep Reinforcement Learning: An Overview. arxiv
  • Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward. arxiv code
  • Deep reinforcement learning from human preferences. arxiv
  • Deep Reinforcement Learning that Matters. arxiv code
  • Device Placement Optimization with Reinforcement Learning. arxiv
  • Distributional Reinforcement Learning with Quantile Regression. arxiv
  • End-to-End Optimization of Task-Oriented Dialogue Model with Deep Reinforcement Learning. arxiv
  • Evolution Strategies as a Scalable Alternative to Reinforcement Learning. arxiv
  • Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning. arxiv
  • Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations. arxiv
  • Learning how to Active Learn: A Deep Reinforcement Learning Approach. arxiv tensorflow
  • Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning. arxiv tensorflow
  • MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence. arxiv code ⭐️
  • Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. arxiv
  • Micro-Objective Learning : Accelerating Deep Reinforcement Learning through the Discovery of Continuous Subgoals. arxiv
  • Neural Architecture Search with Reinforcement Learning. arxiv tensorflow
  • Neural Map: Structured Memory for Deep Reinforcement Learning. arxiv
  • Observational Learning by Reinforcement Learning. arxiv
  • Overcoming Exploration in Reinforcement Learning with Demonstrations. arxiv
  • Practical Network Blocks Design with Q-Learning. arxiv
  • Rainbow: Combining Improvements in Deep Reinforcement Learning. arxiv
  • Reinforcement Learning for Architecture Search by Network Transformation. arxiv code
  • Reinforcement Learning via Recurrent Convolutional Neural Networks. arxiv code
  • Reinforcement Learning with a Corrupted Reward Channel. arxiv ⭐️
  • Reinforcement Learning with Deep Energy-Based Policies. arxiv code
  • Reinforcement Learning with External Knowledge and Two-Stage Q-functions for Predicting Popular Reddit Threads. arxiv
  • Robust Deep Reinforcement Learning with Adversarial Attacks. arxiv
  • Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning. arxiv
  • Shallow Updates for Deep Reinforcement Learning. arxiv code
  • Stochastic Neural Networks for Hierarchical Reinforcement Learning. pdf code
  • Tackling Error Propagation through Reinforcement Learning: A Case of Greedy Dependency Parsing. arxiv code
  • Task-Oriented Query Reformulation with Reinforcement Learning. arxiv code
  • Teaching a Machine to Read Maps with Deep Reinforcement Learning. arxiv code
  • TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning. arxiv code
  • Value Prediction Network. arxiv
  • Variational Deep Q Network. arxiv
  • Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation.arxiv
  • Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning. arxiv

2016

  • Asynchronous Methods for Deep Reinforcement Learning. [arxiv] ⭐️
  • Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning, E. Parisotto, et al., ICLR. [arxiv]
  • A New Softmax Operator for Reinforcement Learning.[url]
  • Benchmarking Deep Reinforcement Learning for Continuous Control, Y. Duan et al., ICML. [arxiv]
  • Better Computer Go Player with Neural Network and Long-term Prediction, Y. Tian et al., ICLR. [arxiv]
  • Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., ICLR. [arxiv]
  • Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks, R. Houthooft et al., arXiv. [url]
  • Control of Memory, Active Perception, and Action in Minecraft, J. Oh et al., ICML. [arxiv]
  • Continuous Deep Q-Learning with Model-based Acceleration, S. Gu et al., ICML. [arxiv]
  • Continuous control with deep reinforcement learning. [arxiv] ⭐️
  • Deep Successor Reinforcement Learning. [arxiv]
  • Dynamic Frame skip Deep Q Network, A. S. Lakshminarayanan et al., IJCAI Deep RL Workshop. [arxiv]
  • Deep Exploration via Bootstrapped DQN. [arxiv] ⭐️
  • Deep Reinforcement Learning for Dialogue Generation. [arxiv] tensorflow
  • Deep Reinforcement Learning in Parameterized Action Space. [arxiv] ⭐️
  • Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments.[url]
  • Designing Neural Network Architectures using Reinforcement Learning. arxiv code
  • Dialogue manager domain adaptation using Gaussian process reinforcement learning. [arxiv]
  • End-to-End Reinforcement Learning of Dialogue Agents for Information Access. [arxiv]
  • Generating Text with Deep Reinforcement Learning. [arxiv]
  • Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization, C. Finn et al., arXiv. [arxiv]
  • Hierarchical Reinforcement Learning using Spatio-Temporal Abstractions and Deep Neural Networks, R. Krishnamurthy et al., arXiv. [arxiv]
  • Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation, T. D. Kulkarni et al., arXiv. [arxiv]
  • Hierarchical Object Detection with Deep Reinforcement Learning. [arxiv]
  • High-Dimensional Continuous Control Using Generalized Advantage Estimation, J. Schulman et al., ICLR. [arxiv]
  • Increasing the Action Gap: New Operators for Reinforcement Learning, M. G. Bellemare et al., AAAI. [arxiv]
  • Interactive Spoken Content Retrieval by Deep Reinforcement Learning. [arxiv]
  • Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection, S. Levine et al., arXiv. [url]
  • Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks, J. N. Foerster et al., arXiv. [url]
  • Learning to compose words into sentences with reinforcement learning. [url]
  • Loss is its own Reward: Self-Supervision for Reinforcement Learning.[arxiv]
  • Model-Free Episodic Control. [arxiv]
  • Mastering the game of Go with deep neural networks and tree search. [nature] ⭐️
  • MazeBase: A Sandbox for Learning from Games .[arxiv]
  • Neural Architecture Search with Reinforcement Learning. [pdf]
  • Neural Combinatorial Optimization with Reinforcement Learning. [arxiv]
  • Non-Deterministic Policy Improvement Stabilizes Approximated Reinforcement Learning. [url]
  • Online Sequence-to-Sequence Active Learning for Open-Domain Dialogue Generation. arXiv. [arxiv]
  • Policy Distillation, A. A. Rusu et at., ICLR. [arxiv]
  • Prioritized Experience Replay. [arxiv] ⭐️
  • Reinforcement Learning Using Quantum Boltzmann Machines. [arxiv]
  • Safe and Efficient Off-Policy Reinforcement Learning, R. Munos et al.[arxiv]
  • Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving. [arxiv]
  • Sample-efficient Deep Reinforcement Learning for Dialog Control. [url]
  • Self-Correcting Models for Model-Based Reinforcement Learning.[url]
  • Unifying Count-Based Exploration and Intrinsic Motivation. [arxiv]
  • Value Iteration Networks. [arxiv]

2015

  • ADAAPT: A Deep Architecture for Adaptive Policy Transfer from Multiple Sources. arxiv
  • Action-Conditional Video Prediction using Deep Networks in Atari Games. arxiv ⭐️
  • Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning. arxiv ⭐️
  • [DDPG] Continuous control with deep reinforcement learning. arxiv ⭐️
  • [NAF] Continuous Deep Q-Learning with Model-based Acceleration. arxiv ⭐️
  • Dueling Network Architectures for Deep Reinforcement Learning. arxiv ⭐️
  • Deep Reinforcement Learning with an Action Space Defined by Natural Language.arxiv
  • Deep Reinforcement Learning with Double Q-learning. arxiv ⭐️
  • Deep Recurrent Q-Learning for Partially Observable MDPs. arxiv ⭐️
  • DeepMPC: Learning Deep Latent Features for Model Predictive Control. pdf
  • Deterministic Policy Gradient Algorithms. pdf ⭐️
  • Dueling Network Architectures for Deep Reinforcement Learning. arxiv
  • End-to-End Training of Deep Visuomotor Policies. arxiv ⭐️
  • Giraffe: Using Deep Reinforcement Learning to Play Chess. arxiv
  • Generating Text with Deep Reinforcement Learning. arxiv
  • How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies. arxiv
  • Human-level control through deep reinforcement learning. nature ⭐️
  • Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models. arxiv ⭐️
  • Learning Simple Algorithms from Examples. arxiv
  • Language Understanding for Text-based Games Using Deep Reinforcement Learning. pdf ⭐️
  • Learning Continuous Control Policies by Stochastic Value Gradients.pdf ⭐️
  • Multiagent Cooperation and Competition with Deep Reinforcement Learning. arxiv
  • Maximum Entropy Deep Inverse Reinforcement Learning. arxiv
  • Massively Parallel Methods for Deep Reinforcement Learning. pdf] ⭐️
  • On Learning to Think- Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models. arxiv
  • Playing Atari with Deep Reinforcement Learning. arxiv
  • Recurrent Reinforcement Learning: A Hybrid Approach. arxiv
  • Strategic Dialogue Management via Deep Reinforcement Learning. arxiv
  • Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control. arxiv
  • Trust Region Policy Optimization. pdf ⭐️
  • Universal Value Function Approximators. pdf
  • Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning. arxiv

2014

  • Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning.[url]

2013

More About

These documents will be updated in sync with my personal blog and knowledge column

  1. CSDN-Blog: A Guide Resource for Deep Reinforcement Learning
  2. ZhiHu-Blog: A Guide Resource for Deep Reinforcement Learning
  3. WeChat(Add account: “NeuronDance”, remark “Name-University/Company”)
    在这里插入图片描述

Cite

Based on the above information, we have made a comprehensive summary of the deep reinforcement of learning materials, and we would like to express our heartfelt thanks to them.

[1].https://github.com/brianspiering/awesome-deep-rl
[2].https://github.com/jgvictores/awesome-deep-reinforcement-learning
[3].https://github.com/PaddlePaddle/PARL/blob/develop/papers/archive.md#distributed-training
[4].https://github.com/LantaoYu/MARL-Papers
[5].https://github.com/gopala-kr/DRL-Agents
[6].https://github.com/junhyukoh/deep-reinforcement-learning-papers
[7].https://www.eff.org/ai/metrics#Source-Code
[8].https://agi.university/the-landscape-of-deep-reinforcement-learning
[9].https://github.com/tigerneil/awesome-deep-rl
[10].https://planspace.org/20170830-berkeley_deep_rl_bootcamp/
[11].https://aikorea.org/awesome-rl/
[12].https://github.com/junhyukoh/deep-reinforcement-learning-papers

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