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A list of recent papers regarding deep reinforcement learning
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README.md
OriginalDeep 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
- Value
- Policy
- Discrete Control
- Continuous Control
- Text Domain
- Visual Domain
- Robotics
- Games
- Monte-Carlo Tree Search
- Inverse Reinforcement Learning
- Improving Exploration
- Multi-Task and Transfer Learning
- Multi-Agent
- Hierarchical Learning
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.
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