Deep RL

Generative Adversarial Imitation Learning NeurIPS 2016 Graying the black box: Understanding DQNs ICML 2016 Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization ICML 2016 Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation NeurIPS 2016 Improving PAC Exploration Using the Median Of Means NeurIPS 2016 Interactive Spoken Content Retrieval by Deep Reinforcement Learning Interspeech 2016 Learning Simple Algorithms from Examples ICML 2016 Learning values across many orders of magnitude NeurIPS 2016 Linear Feature Encoding for Reinforcement Learning NeurIPS 2016 Model-Free Trajectory Optimization for Reinforcement Learning ICML 2016 Near Optimal Behavior via Approximate State Abstraction ICML 2016 Opponent Modeling in Deep Reinforcement Learning ICML 2016 PAC Reinforcement Learning with Rich Observations NeurIPS 2016 Practical Kernel-Based Reinforcement Learning JMLR 2016 Recurrent Attention Models for Depth-Based Person Identification CVPR 2016 Reinforcement Learning for Visual Object Detection CVPR 2016 Safe and Efficient Off-Policy Reinforcement Learning NeurIPS 2016 Strategic Attentive Writer for Learning Macro-Actions NeurIPS 2016 Tree-Structured Reinforcement Learning for Sequential Object Localization NeurIPS 2016 True Online Temporal-Difference Learning JMLR 2016 Unifying Count-Based Exploration and Intrinsic Motivation NeurIPS 2016 Value Iteration Networks NeurIPS 2016 VIME: Variational Information Maximizing Exploration NeurIPS 2016 A Deeper Look at Planning as Learning from Replay ICML 2015 Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games ICML 2015