Deep RL

Extending Model-based Policy Gradients for Robots in Heteroscedastic Environments CoRL 2017 FeUdal Networks for Hierarchical Reinforcement Learning ICML 2017 From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood ACL 2017 Hierarchical Reinforcement Learning with Parameters CoRL 2017 Hindsight Experience Replay NeurIPS 2017 Hybrid Reward Architecture for Reinforcement Learning NeurIPS 2017 Imagination-Augmented Agents for Deep Reinforcement Learning NeurIPS 2017 Improving Reinforcement Learning with Confidence-Based Demonstrations IJCAI 2017 Improving Stochastic Policy Gradients in Continuous Control with Deep Reinforcement Learning using the Beta Distribution ICML 2017 Interactive Narrative Personalization with Deep Reinforcement Learning IJCAI 2017 Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning NeurIPS 2017 Learning Combinatorial Optimization Algorithms over Graphs NeurIPS 2017 Learning Conversational Systems that Interleave Task and Non-Task Content IJCAI 2017 Learning End-to-end Multimodal Sensor Policies for Autonomous Navigation CoRL 2017 Learning how to Active Learn: A Deep Reinforcement Learning Approach EMNLP 2017 Learning in POMDPs with Monte Carlo Tree Search ICML 2017 Learning Sparse Representations in Reinforcement Learning with Sparse Coding IJCAI 2017 Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning IJCNLP 2017 Learning to Learn From Noisy Web Videos CVPR 2017 Learning Unknown Markov Decision Processes: A Thompson Sampling Approach NeurIPS 2017 Learning what to read: Focused machine reading EMNLP 2017 Leveraging Human Knowledge in Tabular Reinforcement Learning: A Study of Human Subjects IJCAI 2017 Mapping Instructions and Visual Observations to Actions with Reinforcement Learning EMNLP 2017 Modular Multitask Reinforcement Learning with Policy Sketches ICML 2017 Multi-Task Deep Reinforcement Learning for Continuous Action Control IJCAI 2017