开源软件名称： junhyukoh/deep-reinforcement-learning-papers开源软件地址： https://github.com/junhyukoh/deep-reinforcement-learning-papers开源编程语言： 开源软件介绍： A list of recent papers regarding deep reinforcement learning.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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