Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A list of recent papers regarding deep reinforcement learning
| Date | Stars |
|---|---|
| 2026-07-31 | 2196 |
| 2026-08-02 | 2196 |
| 2026-08-03 | 2196 |
| 2026-08-06 | 2196 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Deep Reinforcement Learning Papers A list of recent papers regarding deep reinforcement learning. <br> The papers are organized based on manually-defined bookmarks. <br> They are sorted by time to see the recent papers first. <br> Any suggestions and pull requests are welcome. # Bookmarks * [All Papers](#all-papers) * [Value](#value) * [Policy](#policy) * [Discrete Control](#discrete-control) * [Continuous Control](#continuous-control) * [Text Domain](#text-domain) * [Visual Domain](#visual-domain) * [Robotics](#robotics) * [Games](#games) * [Monte-Carlo Tree Search](#monte-carlo-tree-search) * [Inverse Reinforcement Learning](#inverse-reinforcement-learning) * [Improving Exploration](#improving-exploration) * [Multi-Task and Transfer Learning](#multi-task-and-transfer-learning) * [Multi-Agent](#multi-agent) * [Hierarchical Learning](#hierarchical-learning) ## All Papers * [Model-Free Episodic Control](http://arxiv.org/abs/1606.04460), C. Blundell et al., *arXiv*, 2016. * [Safe and Efficient Off-Policy Reinforcement Learning](https://arxiv.org/abs/1606.02647), R. Munos et al., *arXiv*, 2016. * [Deep Successor Reinforcement Learning](http://arxiv.org/abs/1606.02396), T. D. Kulkarni et al., *arXiv*, 2016. * [Unifying Count-Based Exploration and Intrinsic Motivation](https://arxiv.org/abs/1606.01868), M. G. Bellemare et al., *arXiv*, 2016. * [Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks](http://arxiv.org/abs/1605.09674), R. Houthooft et al., *arXiv*, 2016. * [Control of Memory, Active Perception, and Action in Minecraft](http://arxiv.org/abs/1605.09128), J. Oh et al., *ICML*, 2016. * [Dynamic Frame skip Deep Q Network](http://arxiv.org/abs/1605.05365), A. S. Lakshminarayanan et al., *IJCAI Deep RL Workshop*, 2016. * [Hierarchical Reinforcement Learning using Spatio-Temporal Abstractions and Deep Neural Networks](https://arxiv.org/abs/1605.05359), R. Krishnamurthy et al., *arXiv*, 2016. * [Benchmarking Deep Reinforcement Learning for Continuous Control](https://arxiv.org/abs/1604.06778), Y. Duan et al., *ICML*, 2016. * [Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation](https://arxiv.org/abs/1604.06057), T. D. Kulkarni et al., *arXiv*, 2016. * [Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection](http://arxiv.org/abs/1603.02199), S. Levine et al., *arXiv*, 2016. * [Continuous Deep Q-Learning with Model-based Acceleration](http://arxiv.org/abs/1603.00748), S. Gu et al., *ICML*, 2016. * [Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization](http://arxiv.org/abs/1603.00448), C. Finn et al., *arXiv*, 2016. * [Deep Exploration via Bootstrapped DQN](http://arxiv.org/abs/1602.04621), I. Osband et al., *arXiv*, 2016. * [Value Iteration Networks](http://arxiv.org/abs/1602.02867), A. Tamar et al., *arXiv*, 2016. * [Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks](http://arxiv.org/abs/1602.02672), J. N. Foerster et al., *arXiv*, 2016. * [Asynchronous Methods for Deep Reinforcement Learning](http://arxiv.org/abs/1602.01783), V. Mnih et al., *arXiv*, 2016. * [Mastering the game of Go with deep neural networks and tree search](http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html), D. Silver et al., *Nature*, 2016. * [Increasing the Action Gap: New Operators for Reinforcement Learning](http://arxiv.org/abs/1512.04860), M. G. Bellemare et al., *AAAI*, 2016. * [Memory-based control with recurrent neural networks](http://arxiv.org/abs/1512.04455), N. Heess et al., *NIPS Workshop*, 2015. * [How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies](http://arxiv.org/abs/1512.02011), V. François-Lavet et al., *NIPS Workshop*, 2015. * [Multiagent Cooperation and Competition with Deep Reinforcement Learning](http://arxiv.org/abs/1511.08779),
Excerpt of 41,751 characters
Read on GitHub
29
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f87a7b56687217b8, name:reinforcement learning, desc:reinforcement learning