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Deep Reinforcement Learning — Papers (2)

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Deep Reinforcement Learning — Papers

Many recent advancements in AI research stem from breakthroughs in deep reinforcement learning. This is a complex and varied field, but Junhyuk Oh at the University of Michigan has compiled a great list of papers. The list, which originally appeared on GitHub, are sorted by time with most recent appearing first.

Bookmarks

  • All Papers
  • Q-learning
  • Policy Gradient
  • Discrete Control
  • Continuous Control
  • Text Domain
  • Visual Domain
  • Robotics
  • Games
  • Monte-Carlo Tree Search
  • Inverse Reinforcement Learning
  • Improving Exploration
  • Transfer Learning

All Papers

  • Deep Reinforcement Learning with an Unbounded Action Space, J. He et al.,arXiv, 2015.
  • Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., arXiv, 2015.
  • 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.,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.
  • 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.
  • 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.
  • 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.

Q-learning

  • Deep Reinforcement Learning with an Unbounded Action Space, J. He et al.,arXiv, 2015.
  • Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., arXiv, 2015.
  • 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.,arXiv, 2015.
  • 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 Gradient

  • 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.,arXiv, 2015.
  • End-to-End Training of Deep Visuomotor Policies, S. Levine et al., arXiv, 2015.
  • Trust Region Policy Optimization, J. Schulman et al., ICML, 2015.

Discrete Control

  • Deep Reinforcement Learning with an Unbounded Action Space, J. He et al.,arXiv, 2015.
  • Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., arXiv, 2015.
  • 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

  • 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.,arXiv, 2015.
  • Learning Deep Neural Network Policies with Continuous Memory States, M. Zhang et al., arXiv, 2015.
  • 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.

Text Domain

  • Deep Reinforcement Learning with an Unbounded Action Space, 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

  • Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., arXiv, 2015.
  • 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.,arXiv, 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.
  • 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

  • Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control, F. Zhang et al., arXiv, 2015.
  • Learning Deep Neural Network Policies with Continuous Memory States, M. Zhang et al., arXiv, 2015.
  • 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

  • Deep Reinforcement Learning with an Unbounded Action Space, J. He et al.,arXiv, 2015.
  • Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht 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.,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.
  • 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

  • Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning, X. Guo et al., NIPS, 2014.

Inverse Reinforcement Learning

  • Maximum Entropy Deep Inverse Reinforcement Learning, M. Wulfmeier et al.,arXiv, 2015.

Transfer Learning

  • ADAAPT: A Deep Architecture for Adaptive Policy Transfer from Multiple Sources, J. Rajendran et al., arXiv, 2015.

Improving Exploration

  • 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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