<纯干货-5>Deep Reinforcement Learning深度强化学习_论文大集合
来源:互联网 发布:ff14女精灵捏脸数据 编辑:程序博客网 时间:2024/06/06 09:38
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
更多深度学习在NLP方面应用的经典论文、实践经验和最新消息,欢迎关注微信公众号“深度学习与NLP”或“DeepLearning_NLP”或扫描二维码添加关注。
阅读全文
2 0
- <纯干货-5>Deep Reinforcement Learning深度强化学习_论文大集合
- 纯干货11 强化学习(Reinforcement Learning)教材推荐
- 深度强化学习 Deep Reinforcement Learning 学习整理
- 深度强化学习(Deep Reinforcement Learning)的资源
- 深度强化学习(Deep Reinforcement Learning)的资源
- 深度强化学习(Deep Reinforcement Learning)的资源
- 【DQN】解析 DeepMind 深度强化学习 (Deep Reinforcement Learning) 技术
- 深度强化学习(Deep Reinforcement Learning)的资源汇总
- 深度强化学习:入门(Deep Reinforcement Learning: Scratching the surface)
- Deep Reinforcement Learning Papers 强化学习论文集
- 【强化学习】DQN(Deep reinforcement learning) Basic
- Deep Reinforcement Learning for Dialogue Generation-关于生成对话的深度强化学习
- 深度强化学习(Deep Reinforcement Learning)入门:RL base & DQN-DDPG-A3C introduction
- Deep Reinforcement Learning 深度增强学习资源
- Deep Reinforcement Learning 深度增强学习资源
- Deep Reinforcement Learning 深度增强学习资源
- 【DQN】深度增强学习Deep Reinforcement Learning
- Deep Reinforcement Learning 深度增强学习资源
- C++ ---- char,char*,char**,char [],char* []
- Python装饰器学习(九步入门)
- 日期格式化、时间差转换
- 如何免费下载优质的PPT模板?
- 29_面向对象_02_面向对象的方式思考问题
- <纯干货-5>Deep Reinforcement Learning深度强化学习_论文大集合
- http状态码
- Deep Learning论文笔记之(五)CNN卷积神经网络代码理解
- CI 处理json
- Inkpad绘图原理浅析
- $("body").append()是什么意思?
- Eclipse Permission is only granted to system apps 解决
- PID算法的C语言实现五 积分分离的PID优化
- instrument 名词解释