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A list of papers and resources dedicated to deep reinforcement learning
| Date | Stars |
|---|---|
| 2026-07-31 | 837 |
| 2026-08-03 | 837 |
| 2026-08-04 | 837 |
| 2026-08-06 | 837 |
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# Deep Reinforcement Learning Papers A list of papers and resources dedicated to deep reinforcement learning. Please note that this list is currently work-in-progress and far from complete. ## TODOs - Add more and more papers - Improve the way of classifying papers (tags may be useful) - Create a policy of this list: curated or comprehensive, how to define "deep reinforcement learning", etc. ## Contributing If you want to inform the maintainer of a new paper, feel free to contact [@mooopan](https://twitter.com/mooopan). Issues and PRs are also welcome. ## Table of Contents - [Papers](#papers) - [Talks/Slides](#talksslides) - [Miscellaneous](#miscellaneous) ## Papers - [Deep Value Function](#deep-value-function) - [Deep Policy](#deep-policy) - [Deep Actor-Critic](#deep-actor-critic) - [Deep Model](#deep-model) - [Application to Non-RL Tasks](#application-to-non-rl-tasks) - [Unclassified](#unclassified) ### Deep Value Function - S. Lange and M. Riedmiller, **Deep Learning of Visual Control Policies**, ESANN, 2010. [pdf](https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2010-87.pdf) - Deep Fitted Q-Iteration (DFQ) - V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonglou, D. Wierstra, and M. Riedmiller, **Playing Atari with Deep Reinforcement Learning**, NIPS 2013 Deep Learning Workshop, 2013. [pdf](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf) - Deep Q-Network (DQN) with experience replay - V. Mnih, K. Kavukcuoglu, D. Silver, A. a Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis, **Human-level control through deep reinforcement learning**, Nature, 2015. [pdf](http://home.uchicago.edu/~arij/journalclub/papers/2015_Mnih_et_al.pdf) [code](https://sites.google.com/a/deepmind.com/dqn/) - Deep Q-Network (DQN) with experience replay and target network - T. Schaul, D. Horgan, K. Gregor, and D. Silver, **Universal Value Function Approximators**, ICML, 2015. [pdf](http://schaul.site44.com/publications/uvfa.pdf) - A. Nair, P. Srinivasan, S. Blackwell, C. Alcicek, R. Fearon, A. De Maria, M. Suleyman, C. Beattie, S. Petersen, S. Legg, V. Mnih, and D. Silver, **Massively Parallel Methods for Deep Reinforcement Learning**, ICML Deep Learning Workshop, 2015. [pdf](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Publications_files/gorila.pdf) - Gorila (General Reinforcement Learning Architecture) - K. Narasimhan, T. Kulkarni, and R. Barzilay, **Language Understanding for Text-based Games Using Deep Reinforcement Learning**, EMNLP, 2015. [pdf](http://people.csail.mit.edu/karthikn/pdfs/mud-play15.pdf) [supplementary](http://people.csail.mit.edu/karthikn/pdfs/mud-supp.pdf) [code](http://people.csail.mit.edu/karthikn/mud-play/) - LSTM-DQN - M. Hausknecht and P. Stone, **Deep Recurrent Q-Learning for Partially Observable MDPs**, arXiv, 2015. [arXiv](http://arxiv.org/abs/1507.06527) [code](https://github.com/mhauskn/dqn/tree/recurrent) - M. Lai, **Giraffe: Using Deep Reinforcement Learning to Play Chess**, arXiv. 2015. [arXiv](http://arxiv.org/abs/1509.01549) [code](https://bitbucket.org/waterreaction/giraffe) - H. van Hasselt, A. Guez, and D. Silver, **Deep reinforcement learning with double q-learning**, arXiv, 2015. [arXiv](http://arxiv.org/abs/1509.06461) - Double DQN - F. Zhang, J. Leitner, M. Milford, B. Upcroft, and P. Corke, **Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control**, in ACRA, 2015. [pdf](http://juxi.net/papers/others/zhang2015acra-submission.pdf) - T. Schaul, J. Quan, I. Antonoglou, and D. Silver, **Prioritized Experience Replay**, arXiv, 2015. [arXiv](http://arxiv.org/abs/1511.05952) - Z. Wang, N. de Freitas, and M. Lanctot, **Dueling Network Architectures for Deep Reinforcement Learning**, arXiv, 2015. [arXiv](http://arxiv.org/abs/1511.06581) - V. François-Lavet, R. Fonteneau, and D. Ernst, **H
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Yasuhiro Fujita · @pfnet
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:63194f31f9bc99ab, name:reinforcement learning, desc:reinforcement learning