ICML2016 reinforcement-learning-related papers

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Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Learning Simple Algorithms from Examples
Stability of Controllers for Gaussian Process Forward Models
Smooth Imitation Learning for Online Sequence Prediction
On the Analysis of Complex Backup Strategies in Monte Carlo Tree Search
Benchmarking Deep Reinforcement Learning for Continuous Control
Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control
Why Most Decisions Are Easy in Tetris—And Perhaps in Other Sequential Decision Problems, As Well
Opponent Modeling in Deep Reinforcement Learning
Softened Approximate Policy Iteration for Markov Games
Graying the black box: Understanding DQNs
Asynchronous Methods for Deep Reinforcement Learning
Dueling Network Architectures for Deep Reinforcement Learning
Differentially Private Policy Evaluation
Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning
Hierarchical Decision Making In Electricity Grid Management
Generalization and Exploration via Randomized Value Functions
Model-Free Imitation Learning with Policy Optimization
Control of Memory, Active Perception, and Action in Minecraft
Continuous Deep Q-Learning with Model-based Acceleration
Near Optimal Behavior via Approximate State Abstraction
Model-Free Trajectory Optimization for Reinforcement Learning