Deep Reinforcement Learning 文献综述

 转载地址：http://blog.csdn.net/lqfarmer/article/details/72868471

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目录

值函数

策略 

离散控制

连续控制 

多智能体

文本处理

计算机视觉

机器人

游戏

蒙特卡洛

逆强化学习

多任务和迁移学习

搜索优化

层次化学习相关的文章

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值函数

• 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.

策略 

• 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.

离散控制

• 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.

连续控制 

• 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.

多智能体

• 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.

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文本处理

• 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

• 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.

机器人

• 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.

游戏

• 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.

蒙特卡洛

• 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

• 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

• 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

• 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.

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.