本文罗列了最近放出来的关于深度强化学习（Deep Reinforcement Learning，DRL）的一些论文。文章采用人工定义的方式来进行组织，按照时间的先后进行排序，越新的论文，排在越前面。希望对大家有用，同时欢迎大家提交自己阅读过的论文。

目录

•    值函数相关的文章

•    策略相关的文章

•    离散控制相关的文章

•    连续控制相关的文章

•    文本处理领域相关的文章

•    计算机视觉领域相关的文章

•    机器人领域相关的文章

•    游戏领域相关的文章

•    蒙特卡洛树搜索相关的文章

•    逆强化学习相关的文章

•    搜索优化相关的文章

•    多任务和迁移学习相关的文章

•    多智能体相关的文章

•    层次化学习相关的文章

值函数相关的文章

  • 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.

策略相关的文章

  • 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.

离散控制相关的文章

  • 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.

连续控制相关的文章

  • 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.

文本处理领域相关的文章

  • 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.

计算机视觉领域相关的文章

  • 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.

机器人领域相关的文章

  • 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.

游戏领域相关的文章

  • 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.

蒙特卡洛树搜索相关的文章

  • 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.

逆强化学习相关的文章

  • 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.

多任务和迁移学习相关的文章

  • 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.

搜索优化相关的文章

  • 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.

多智能体相关的文章

  • 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.

层次化学习相关的文章

  • 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.

原文链接：https://github.com/junhyukoh/deep-reinforcement-learning-papers

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