Deep Reinforcement Learning Papers

A list of recent papers regarding deep reinforcement learning.
The papers are organized based on manually-defined bookmarks.
They are sorted by time to see the recent papers first.
Any suggestions and pull requests are welcome.

Bookmarks

All Papers

Model-Free Episodic Control, C. Blundell et al., arXiv, 2016.
Safe and Efficient Off-Policy Reinforcement Learning, R. Munos et al., arXiv, 2016.
Deep Successor Reinforcement Learning, T. D. Kulkarni et al., arXiv, 2016.
Unifying Count-Based Exploration and Intrinsic Motivation, M. G. Bellemare et al., arXiv, 2016.
Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks, R. Houthooft et al., arXiv, 2016.
Control of Memory, Active Perception, and Action in Minecraft, J. Oh et al., ICML, 2016.
Dynamic Frame skip Deep Q Network, A. S. Lakshminarayanan et al., IJCAI Deep RL Workshop, 2016.
Hierarchical Reinforcement Learning using Spatio-Temporal Abstractions and Deep Neural Networks, R. Krishnamurthy et al., arXiv, 2016.
Benchmarking Deep Reinforcement Learning for Continuous Control, Y. Duan et al., ICML, 2016.
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation, T. D. Kulkarni et al., arXiv, 2016.
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection, S. Levine et al., arXiv, 2016.
Continuous Deep Q-Learning with Model-based Acceleration, S. Gu et al., ICML, 2016.
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization, C. Finn et al., arXiv, 2016.
Deep Exploration via Bootstrapped DQN, I. Osband et al., arXiv, 2016.
Value Iteration Networks, A. Tamar et al., arXiv, 2016.
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks, J. N. Foerster et al., arXiv, 2016.
Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
Increasing the Action Gap: New Operators for Reinforcement Learning, M. G. Bellemare et al., AAAI, 2016.
Memory-based control with recurrent neural networks, N. Heess et al., NIPS Workshop, 2015.
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies, V. François-Lavet et al., NIPS Workshop, 2015.
Multiagent Cooperation and Competition with Deep Reinforcement Learning, A. Tampuu et al., arXiv, 2015.
Strategic Dialogue Management via Deep Reinforcement Learning, H. Cuayáhuitl et al., NIPS Workshop, 2015.
MazeBase: A Sandbox for Learning from Games, S. Sukhbaatar et al., arXiv, 2016.
Learning Simple Algorithms from Examples, W. Zaremba et al., arXiv, 2015.
Dueling Network Architectures for Deep Reinforcement Learning, Z. Wang et al., arXiv, 2015.
Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning, E. Parisotto, et al., ICLR, 2016.
Better Computer Go Player with Neural Network and Long-term Prediction, Y. Tian et al., ICLR, 2016.
Policy Distillation, A. A. Rusu et at., ICLR, 2016.
Prioritized Experience Replay, T. Schaul et al., ICLR, 2016.
Deep Reinforcement Learning with an Action Space Defined by Natural Language, J. He et al., arXiv, 2015.
Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., ICLR, 2016.
Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control, F. Zhang et al., arXiv, 2015.
Generating Text with Deep Reinforcement Learning, H. Guo, arXiv, 2015.
ADAAPT: A Deep Architecture for Adaptive Policy Transfer from Multiple Sources, J. Rajendran et al., arXiv, 2015.
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning, S. Mohamed and D. J. Rezende, arXiv, 2015.
Deep Reinforcement Learning with Double Q-learning, H. van Hasselt et al., arXiv, 2015.
Recurrent Reinforcement Learning: A Hybrid Approach, X. Li et al., arXiv, 2015.
Continuous control with deep reinforcement learning, T. P. Lillicrap et al., ICLR, 2016.
Language Understanding for Text-based Games Using Deep Reinforcement Learning, K. Narasimhan et al., EMNLP, 2015.
Giraffe: Using Deep Reinforcement Learning to Play Chess, M. Lai, arXiv, 2015.
Action-Conditional Video Prediction using Deep Networks in Atari Games, J. Oh et al., NIPS, 2015.
Learning Continuous Control Policies by Stochastic Value Gradients, N. Heess et al., NIPS, 2015.
Learning Deep Neural Network Policies with Continuous Memory States, M. Zhang et al., arXiv, 2015.
Deep Recurrent Q-Learning for Partially Observable MDPs, M. Hausknecht and P. Stone, arXiv, 2015.
Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences, H. Mei et al., arXiv, 2015.
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models, B. C. Stadie et al., arXiv, 2015.
Maximum Entropy Deep Inverse Reinforcement Learning, M. Wulfmeier et al., arXiv, 2015.
High-Dimensional Continuous Control Using Generalized Advantage Estimation, J. Schulman et al., ICLR, 2016.
End-to-End Training of Deep Visuomotor Policies, S. Levine et al., arXiv, 2015.
DeepMPC: Learning Deep Latent Features for Model Predictive Control, I. Lenz, et al., RSS, 2015.
Universal Value Function Approximators, T. Schaul et al., ICML, 2015.
Deterministic Policy Gradient Algorithms, D. Silver et al., ICML, 2015.
Massively Parallel Methods for Deep Reinforcement Learning, A. Nair et al., ICML Workshop, 2015.
Trust Region Policy Optimization, J. Schulman et al., ICML, 2015.
Human-level control through deep reinforcement learning, V. Mnih et al., Nature, 2015.
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning, X. Guo et al., NIPS, 2014.
Playing Atari with Deep Reinforcement Learning, V. Mnih et al., NIPS Workshop, 2013.

Value

Model-Free Episodic Control, C. Blundell et al., arXiv, 2016.
Safe and Efficient Off-Policy Reinforcement Learning, R. Munos et al., arXiv, 2016.
Deep Successor Reinforcement Learning, T. D. Kulkarni et al., arXiv, 2016.
Unifying Count-Based Exploration and Intrinsic Motivation, M. G. Bellemare et al., arXiv, 2016.
Control of Memory, Active Perception, and Action in Minecraft, J. Oh et al., ICML, 2016.
Dynamic Frame skip Deep Q Network, A. S. Lakshminarayanan et al., IJCAI Deep RL Workshop, 2016.
Hierarchical Reinforcement Learning using Spatio-Temporal Abstractions and Deep Neural Networks, R. Krishnamurthy et al., arXiv, 2016.
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation, T. D. Kulkarni et al., arXiv, 2016.
Continuous Deep Q-Learning with Model-based Acceleration, S. Gu et al., ICML, 2016.
Deep Exploration via Bootstrapped DQN, I. Osband et al., arXiv, 2016.
Value Iteration Networks, A. Tamar et al., arXiv, 2016.
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks, J. N. Foerster et al., arXiv, 2016.
Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
Increasing the Action Gap: New Operators for Reinforcement Learning, M. G. Bellemare et al., AAAI, 2016.
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies, V. François-Lavet et al., NIPS Workshop, 2015.
Multiagent Cooperation and Competition with Deep Reinforcement Learning, A. Tampuu et al., arXiv, 2015.
Strategic Dialogue Management via Deep Reinforcement Learning, H. Cuayáhuitl et al., NIPS Workshop, 2015.
Learning Simple Algorithms from Examples, W. Zaremba et al., arXiv, 2015.
Dueling Network Architectures for Deep Reinforcement Learning, Z. Wang et al., arXiv, 2015.
Prioritized Experience Replay, T. Schaul et al., ICLR, 2016.
Deep Reinforcement Learning with an Action Space Defined by Natural Language, J. He et al., arXiv, 2015.
Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., ICLR, 2016.
Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control, F. Zhang et al., arXiv, 2015.
Generating Text with Deep Reinforcement Learning, H. Guo, arXiv, 2015.
Deep Reinforcement Learning with Double Q-learning, H. van Hasselt et al., arXiv, 2015.
Recurrent Reinforcement Learning: A Hybrid Approach, X. Li et al., arXiv, 2015.
Continuous control with deep reinforcement learning, T. P. Lillicrap et al., ICLR, 2016.
Language Understanding for Text-based Games Using Deep Reinforcement Learning, K. Narasimhan et al., EMNLP, 2015.
Action-Conditional Video Prediction using Deep Networks in Atari Games, J. Oh et al., NIPS, 2015.
Deep Recurrent Q-Learning for Partially Observable MDPs, M. Hausknecht and P. Stone, arXiv, 2015.
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models, B. C. Stadie et al., arXiv, 2015.
Massively Parallel Methods for Deep Reinforcement Learning, A. Nair et al., ICML Workshop, 2015.
Human-level control through deep reinforcement learning, V. Mnih et al., Nature, 2015.
Playing Atari with Deep Reinforcement Learning, V. Mnih et al., NIPS Workshop, 2013.

Policy

Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks, R. Houthooft et al., arXiv, 2016.
Benchmarking Deep Reinforcement Learning for Continuous Control, Y. Duan et al., ICML, 2016.
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection, S. Levine et al., arXiv, 2016.
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization, C. Finn et al., arXiv, 2016.
Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
Memory-based control with recurrent neural networks, N. Heess et al., NIPS Workshop, 2015.
MazeBase: A Sandbox for Learning from Games, S. Sukhbaatar et al., arXiv, 2016.
ADAAPT: A Deep Architecture for Adaptive Policy Transfer from Multiple Sources, J. Rajendran et al., arXiv, 2015.
Continuous control with deep reinforcement learning, T. P. Lillicrap et al., ICLR, 2016.
Learning Continuous Control Policies by Stochastic Value Gradients, N. Heess et al., NIPS, 2015.
High-Dimensional Continuous Control Using Generalized Advantage Estimation, J. Schulman et al., ICLR, 2016.
End-to-End Training of Deep Visuomotor Policies, S. Levine et al., arXiv, 2015.
Deterministic Policy Gradient Algorithms, D. Silver et al., ICML, 2015.
Trust Region Policy Optimization, J. Schulman et al., ICML, 2015.

Discrete Control

Model-Free Episodic Control, C. Blundell et al., arXiv, 2016.
Safe and Efficient Off-Policy Reinforcement Learning, R. Munos et al., arXiv, 2016.
Deep Successor Reinforcement Learning, T. D. Kulkarni et al., arXiv, 2016.
Unifying Count-Based Exploration and Intrinsic Motivation, M. G. Bellemare et al., arXiv, 2016.
Control of Memory, Active Perception, and Action in Minecraft, J. Oh et al., ICML, 2016.
Dynamic Frame skip Deep Q Network, A. S. Lakshminarayanan et al., IJCAI Deep RL Workshop, 2016.
Hierarchical Reinforcement Learning using Spatio-Temporal Abstractions and Deep Neural Networks, R. Krishnamurthy et al., arXiv, 2016.
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation, T. D. Kulkarni et al., arXiv, 2016.
Deep Exploration via Bootstrapped DQN, I. Osband et al., arXiv, 2016.
Value Iteration Networks, A. Tamar et al., arXiv, 2016.
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks, J. N. Foerster et al., arXiv, 2016.
Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
Increasing the Action Gap: New Operators for Reinforcement Learning, M. G. Bellemare et al., AAAI, 2016.
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies, V. François-Lavet et al., NIPS Workshop, 2015.
Multiagent Cooperation and Competition with Deep Reinforcement Learning, A. Tampuu et al., arXiv, 2015.
Strategic Dialogue Management via Deep Reinforcement Learning, H. Cuayáhuitl et al., NIPS Workshop, 2015.
Learning Simple Algorithms from Examples, W. Zaremba et al., arXiv, 2015.
Dueling Network Architectures for Deep Reinforcement Learning, Z. Wang et al., arXiv, 2015.
Better Computer Go Player with Neural Network and Long-term Prediction, Y. Tian et al., ICLR, 2016.
Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning, E. Parisotto, et al., ICLR, 2016.
Policy Distillation, A. A. Rusu et at., ICLR, 2016.
Prioritized Experience Replay, T. Schaul et al., ICLR, 2016.
Deep Reinforcement Learning with an Action Space Defined by Natural Language, J. He et al., arXiv, 2015.
Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., ICLR, 2016.
Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control, F. Zhang et al., arXiv, 2015.
Generating Text with Deep Reinforcement Learning, H. Guo, arXiv, 2015.
ADAAPT: A Deep Architecture for Adaptive Policy Transfer from Multiple Sources, J. Rajendran et al., arXiv, 2015.
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning, S. Mohamed and D. J. Rezende, arXiv, 2015.
Deep Reinforcement Learning with Double Q-learning, H. van Hasselt et al., arXiv, 2015.
Recurrent Reinforcement Learning: A Hybrid Approach, X. Li et al., arXiv, 2015.
Language Understanding for Text-based Games Using Deep Reinforcement Learning, K. Narasimhan et al., EMNLP, 2015.
Giraffe: Using Deep Reinforcement Learning to Play Chess, M. Lai, arXiv, 2015.
Action-Conditional Video Prediction using Deep Networks in Atari Games, J. Oh et al., NIPS, 2015.
Deep Recurrent Q-Learning for Partially Observable MDPs, M. Hausknecht and P. Stone, arXiv, 2015.
Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences, H. Mei et al., arXiv, 2015.
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models, B. C. Stadie et al., arXiv, 2015.
Universal Value Function Approximators, T. Schaul et al., ICML, 2015.
Massively Parallel Methods for Deep Reinforcement Learning, A. Nair et al., ICML Workshop, 2015.
Human-level control through deep reinforcement learning, V. Mnih et al., Nature, 2015.
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning, X. Guo et al., NIPS, 2014.
Playing Atari with Deep Reinforcement Learning, V. Mnih et al., NIPS Workshop, 2013.

Continuous Control

Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks, R. Houthooft et al., arXiv, 2016.
Benchmarking Deep Reinforcement Learning for Continuous Control, Y. Duan et al., ICML, 2016.
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection, S. Levine et al., arXiv, 2016.
Continuous Deep Q-Learning with Model-based Acceleration, S. Gu et al., ICML, 2016.
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization, C. Finn et al., arXiv, 2016.
Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
Memory-based control with recurrent neural networks, N. Heess et al., NIPS Workshop, 2015.
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning, S. Mohamed and D. J. Rezende, arXiv, 2015.
Continuous control with deep reinforcement learning, T. P. Lillicrap et al., ICLR, 2016.
Learning Continuous Control Policies by Stochastic Value Gradients, N. Heess et al., NIPS, 2015.
Learning Deep Neural Network Policies with Continuous Memory States, M. Zhang et al., arXiv, 2015.
High-Dimensional Continuous Control Using Generalized Advantage Estimation, J. Schulman et al., ICLR, 2016.
End-to-End Training of Deep Visuomotor Policies, S. Levine et al., arXiv, 2015.
DeepMPC: Learning Deep Latent Features for Model Predictive Control, I. Lenz, et al., RSS, 2015.
Deterministic Policy Gradient Algorithms, D. Silver et al., ICML, 2015.
Trust Region Policy Optimization, J. Schulman et al., ICML, 2015.

Text Domain

Strategic Dialogue Management via Deep Reinforcement Learning, H. Cuayáhuitl et al., NIPS Workshop, 2015.
MazeBase: A Sandbox for Learning from Games, S. Sukhbaatar et al., arXiv, 2016.
Deep Reinforcement Learning with an Action Space Defined by Natural Language, J. He et al., arXiv, 2015.
Generating Text with Deep Reinforcement Learning, H. Guo, arXiv, 2015.
Language Understanding for Text-based Games Using Deep Reinforcement Learning, K. Narasimhan et al., EMNLP, 2015.
Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences, H. Mei et al., arXiv, 2015.

Visual Domain

Model-Free Episodic Control, C. Blundell et al., arXiv, 2016.
Deep Successor Reinforcement Learning, T. D. Kulkarni et al., arXiv, 2016.
Unifying Count-Based Exploration and Intrinsic Motivation, M. G. Bellemare et al., arXiv, 2016.
Control of Memory, Active Perception, and Action in Minecraft, J. Oh et al., ICML, 2016.
Dynamic Frame skip Deep Q Network, A. S. Lakshminarayanan et al., IJCAI Deep RL Workshop, 2016.
Hierarchical Reinforcement Learning using Spatio-Temporal Abstractions and Deep Neural Networks, R. Krishnamurthy et al., arXiv, 2016.
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation, T. D. Kulkarni et al., arXiv, 2016.
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection, S. Levine et al., arXiv, 2016.
Deep Exploration via Bootstrapped DQN, I. Osband et al., arXiv, 2016.
Value Iteration Networks, A. Tamar et al., arXiv, 2016.
Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
Increasing the Action Gap: New Operators for Reinforcement Learning, M. G. Bellemare et al., AAAI, 2016.
Memory-based control with recurrent neural networks, N. Heess et al., NIPS Workshop, 2015.
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies, V. François-Lavet et al., NIPS Workshop, 2015.
Multiagent Cooperation and Competition with Deep Reinforcement Learning, A. Tampuu et al., arXiv, 2015.
Dueling Network Architectures for Deep Reinforcement Learning, Z. Wang et al., arXiv, 2015.
Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning, E. Parisotto, et al., ICLR, 2016.
Better Computer Go Player with Neural Network and Long-term Prediction, Y. Tian et al., ICLR, 2016.
Policy Distillation, A. A. Rusu et at., ICLR, 2016.
Prioritized Experience Replay, T. Schaul et al., ICLR, 2016.
Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., ICLR, 2016.
Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control, F. Zhang et al., arXiv, 2015.
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning, S. Mohamed and D. J. Rezende, arXiv, 2015.
Deep Reinforcement Learning with Double Q-learning, H. van Hasselt et al., arXiv, 2015.
Continuous control with deep reinforcement learning, T. P. Lillicrap et al., ICLR, 2016.
Giraffe: Using Deep Reinforcement Learning to Play Chess, M. Lai, arXiv, 2015.
Action-Conditional Video Prediction using Deep Networks in Atari Games, J. Oh et al., NIPS, 2015.
Learning Continuous Control Policies by Stochastic Value Gradients, N. Heess et al., NIPS, 2015.
Deep Recurrent Q-Learning for Partially Observable MDPs, M. Hausknecht and P. Stone, arXiv, 2015.
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models, B. C. Stadie et al., arXiv, 2015.
High-Dimensional Continuous Control Using Generalized Advantage Estimation, J. Schulman et al., ICLR, 2016.
End-to-End Training of Deep Visuomotor Policies, S. Levine et al., arXiv, 2015.
Universal Value Function Approximators, T. Schaul et al., ICML, 2015.
Massively Parallel Methods for Deep Reinforcement Learning, A. Nair et al., ICML Workshop, 2015.
Trust Region Policy Optimization, J. Schulman et al., ICML, 2015.
Human-level control through deep reinforcement learning, V. Mnih et al., Nature, 2015.
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning, X. Guo et al., NIPS, 2014.
Playing Atari with Deep Reinforcement Learning, V. Mnih et al., NIPS Workshop, 2013.

Robotics

Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks, R. Houthooft et al., arXiv, 2016.
Benchmarking Deep Reinforcement Learning for Continuous Control, Y. Duan et al., ICML, 2016.
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection, S. Levine et al., arXiv, 2016.
Continuous Deep Q-Learning with Model-based Acceleration, S. Gu et al., ICML, 2016.
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization, C. Finn et al., arXiv, 2016.
Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
Memory-based control with recurrent neural networks, N. Heess et al., NIPS Workshop, 2015.
Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control, F. Zhang et al., arXiv, 2015.
Learning Continuous Control Policies by Stochastic Value Gradients, N. Heess et al., NIPS, 2015.
Learning Deep Neural Network Policies with Continuous Memory States, M. Zhang et al., arXiv, 2015.
High-Dimensional Continuous Control Using Generalized Advantage Estimation, J. Schulman et al., ICLR, 2016.
End-to-End Training of Deep Visuomotor Policies, S. Levine et al., arXiv, 2015.
DeepMPC: Learning Deep Latent Features for Model Predictive Control, I. Lenz, et al., RSS, 2015.
Trust Region Policy Optimization, J. Schulman et al., ICML, 2015.

Games

Model-Free Episodic Control, C. Blundell et al., arXiv, 2016.
Safe and Efficient Off-Policy Reinforcement Learning, R. Munos et al., arXiv, 2016.
Deep Successor Reinforcement Learning, T. D. Kulkarni et al., arXiv, 2016.
Unifying Count-Based Exploration and Intrinsic Motivation, M. G. Bellemare et al., arXiv, 2016.
Control of Memory, Active Perception, and Action in Minecraft, J. Oh et al., ICML, 2016.
Dynamic Frame skip Deep Q Network, A. S. Lakshminarayanan et al., IJCAI Deep RL Workshop, 2016.
Hierarchical Reinforcement Learning using Spatio-Temporal Abstractions and Deep Neural Networks, R. Krishnamurthy et al., arXiv, 2016.
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation, T. D. Kulkarni et al., arXiv, 2016.
Deep Exploration via Bootstrapped DQN, I. Osband et al., arXiv, 2016.
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks, J. N. Foerster et al., arXiv, 2016.
Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
Increasing the Action Gap: New Operators for Reinforcement Learning, M. G. Bellemare et al., AAAI, 2016.
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies, V. François-Lavet et al., NIPS Workshop, 2015.
Multiagent Cooperation and Competition with Deep Reinforcement Learning, A. Tampuu et al., arXiv, 2015.
MazeBase: A Sandbox for Learning from Games, S. Sukhbaatar et al., arXiv, 2016.
Dueling Network Architectures for Deep Reinforcement Learning, Z. Wang et al., arXiv, 2015.
Better Computer Go Player with Neural Network and Long-term Prediction, Y. Tian et al., ICLR, 2016.
Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning, E. Parisotto, et al., ICLR, 2016.
Policy Distillation, A. A. Rusu et at., ICLR, 2016.
Prioritized Experience Replay, T. Schaul et al., ICLR, 2016.
Deep Reinforcement Learning with an Action Space Defined by Natural Language, J. He et al., arXiv, 2015.
Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., ICLR, 2016.
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning, S. Mohamed and D. J. Rezende, arXiv, 2015.
Deep Reinforcement Learning with Double Q-learning, H. van Hasselt et al., arXiv, 2015.
Continuous control with deep reinforcement learning, T. P. Lillicrap et al., ICLR, 2016.
Language Understanding for Text-based Games Using Deep Reinforcement Learning, K. Narasimhan et al., EMNLP, 2015.
Giraffe: Using Deep Reinforcement Learning to Play Chess, M. Lai, arXiv, 2015.
Action-Conditional Video Prediction using Deep Networks in Atari Games, J. Oh et al., NIPS, 2015.
Deep Recurrent Q-Learning for Partially Observable MDPs, M. Hausknecht and P. Stone, arXiv, 2015.
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models, B. C. Stadie et al., arXiv, 2015.
Universal Value Function Approximators, T. Schaul et al., ICML, 2015.
Massively Parallel Methods for Deep Reinforcement Learning, A. Nair et al., ICML Workshop, 2015.
Trust Region Policy Optimization, J. Schulman et al., ICML, 2015.
Human-level control through deep reinforcement learning, V. Mnih et al., Nature, 2015.
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning, X. Guo et al., NIPS, 2014.
Playing Atari with Deep Reinforcement Learning, V. Mnih et al., NIPS Workshop, 2013.

Monte-Carlo Tree Search

Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
Better Computer Go Player with Neural Network and Long-term Prediction, Y. Tian et al., ICLR, 2016.
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning, X. Guo et al., NIPS, 2014.

Inverse Reinforcement Learning

Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization, C. Finn et al., arXiv, 2016.
Maximum Entropy Deep Inverse Reinforcement Learning, M. Wulfmeier et al., arXiv, 2015.

Multi-Task and Transfer Learning

Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning, E. Parisotto, et al., ICLR, 2016.
Policy Distillation, A. A. Rusu et at., ICLR, 2016.
ADAAPT: A Deep Architecture for Adaptive Policy Transfer from Multiple Sources, J. Rajendran et al., arXiv, 2015.
Universal Value Function Approximators, T. Schaul et al., ICML, 2015.

Improving Exploration

Unifying Count-Based Exploration and Intrinsic Motivation, M. G. Bellemare et al., arXiv, 2016.
Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks, R. Houthooft et al., arXiv, 2016.
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation, T. D. Kulkarni et al., arXiv, 2016.
Deep Exploration via Bootstrapped DQN, I. Osband et al., arXiv, 2016.
Action-Conditional Video Prediction using Deep Networks in Atari Games, J. Oh et al., NIPS, 2015.
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models, B. C. Stadie et al., arXiv, 2015.

Multi-Agent

Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks, J. N. Foerster et al., arXiv, 2016.
Multiagent Cooperation and Competition with Deep Reinforcement Learning, A. Tampuu et al., arXiv, 2015.

Hierarchical Learning

Deep Successor Reinforcement Learning, T. D. Kulkarni et al., arXiv, 2016.
Hierarchical Reinforcement Learning using Spatio-Temporal Abstractions and Deep Neural Networks, R. Krishnamurthy et al., arXiv, 2016.
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation, T. D. Kulkarni et al., arXiv, 2016.

(zhuan) Deep Reinforcement Learning Papers的更多相关文章

(转) Deep Reinforcement Learning: Playing a Racing Game
Byte Tank Posts Archive Deep Reinforcement Learning: Playing a Racing Game OCT 6TH, 2016 Agent playi ...
(转) Deep Reinforcement Learning: Pong from Pixels
Andrej Karpathy blog About Hacker's guide to Neural Networks Deep Reinforcement Learning: Pong from ...
论文笔记之：Asynchronous Methods for Deep Reinforcement Learning
Asynchronous Methods for Deep Reinforcement Learning ICML 2016 深度强化学习最近被人发现貌似不太稳定,有人提出很多改善的方法,这些方法有很 ...
【资料总结】| Deep Reinforcement Learning 深度强化学习
在机器学习中,我们经常会分类为有监督学习和无监督学习,但是尝尝会忽略一个重要的分支,强化学习.有监督学习和无监督学习非常好去区分,学习的目标,有无标签等都是区分标准.如果说监督学习的目标是预测,那么强 ...
Deep Reinforcement Learning
Reinforcement-Learning-Introduction-Adaptive-Computation http://incompleteideas.net/book/bookdraft20 ...
Deep Reinforcement Learning with Iterative Shift for Visual Tracking
Deep Reinforcement Learning with Iterative Shift for Visual Tracking 2019-07-30 14:55:31 Paper: http ...
深度强化学习（Deep Reinforcement Learning）入门：RL base & DQN-DDPG-A3C introduction
转自https://zhuanlan.zhihu.com/p/25239682 过去的一段时间在深度强化学习领域投入了不少精力,工作中也在应用DRL解决业务问题.子曰:温故而知新,在进一步深入研究和应 ...
(转) Playing FPS games with deep reinforcement learning
Playing FPS games with deep reinforcement learning 博文转自:https://blog.acolyer.org/2016/11/23/playing- ...
Learning Roadmap of Deep Reinforcement Learning
1. 知乎上关于DQN入门的系列文章 1.1 DQN 从入门到放弃 DQN 从入门到放弃1 DQN与增强学习 DQN 从入门到放弃2 增强学习与MDP DQN 从入门到放弃3 价值函数与Bellman ...

随机推荐

ACM_1001_Exponentiation 详解
参考:http://blog.csdn.net/rually/article/details/8585268 #include<iostream> using namespace std; ...
NSOperation的几种使用方式
1.NSInvocationOperation 创建NSInvocationOperation对象 - (id)initWithTarget:(id)target selector:(SEL)sel ...
使用FlaycoBanner实现图片轮播效果（加载网络图片）
FlaycoBanner是一个开源图片轮播框架,支持android2.2及以上: git地址:https://github.com/H07000223/FlycoBanner_Master 在andr ...
BackTrack5-r3配置网络信息
设置静态IP在BT终端输入:ifconfig -a 按回车// 查看所有网卡在BT终端输入:vi /etc/network/interfaces ...
OD18
介绍一个工具exescope 可以修改一些exe程序里的东西通过这个工具我们找到了我们要除掉的NAG窗口的具体位置那我们可以通过OD进行跟踪来到程序头下段 ...
POM
代码的第一行是xml头,指定了该xml文档的版本和编码方式 project是所有pom.xml的根元素,还声明了一些POM相关的命名空间及xsd元素. modelVersion指定了当前POM模型的版 ...
HDU 5398 （动态树）
Problem GCD Tree 题目大意 n个点的无向完全图,标号1~n,每条边u-->v 的权值为gcd(u,v),求其最大生成树,输出最大边权和. n<=10^5,有多个询问. 解题 ...
Echarts 动态折线图
<script src="http://echarts.baidu.com/build/dist/echarts-all.js"></script>< ...
iOS给UIimage添加圆角的两种方式
众所周知,给图片添加圆角有CALayer的cornerRadius, 比如: 最直接的方法: imgView.layer.cornerRadius1=110; imgView.clipsToBou ...
win32自绘按钮，使用GDI+（二）
一.解决上一篇的两个问题. 1.按钮背景透明方法是,在绘制按钮之前,向按钮的父窗口发生WM_CTLCOLORBTN消息.该消息返回一个画刷句柄,系统使用该画刷句柄画出按钮的背景.所以我们在处理这个消 ...

(zhuan) Deep Reinforcement Learning Papers