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【论文整理】最全深度强化学习论文集！强化学习必读论文！

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
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.

交叉熵损失函数（Cross-Entropy Loss）我叫罗泽南深度学习人工智能
原理交叉熵损失函数是深度学习中分类问题常用的损失函数，特别适用于多分类问题。它通过度量预测分布与真实分布之间的差异，来衡量模型输出的准确性。交叉熵的数学公式交叉熵的定义如下：CrossEntroyLoss=−∑i=1Nyi⋅log(y^i)\begin{equation}CrossEntroyLoss=-\sum_{i=1}^{N}y_i\cdotlog(\hat{y}_i)\end{equati
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