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Deep RL

3861 directly classified papers

Papers per year

Papers

On Thompson Sampling and Asymptotic Optimality IJCAI 2017
Reinforcement Learning with a Corrupted Reward Channel IJCAI 2017
Learning Conversational Systems that Interleave Task and Non-Task Content IJCAI 2017
Interactive Narrative Personalization with Deep Reinforcement Learning IJCAI 2017
A Monte Carlo Tree Search approach to Active Malware Analysis IJCAI 2017
Leveraging Human Knowledge in Tabular Reinforcement Learning: A Study of Human Subjects IJCAI 2017
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents IJCAI 2017
Tensor Based Knowledge Transfer Across Skill Categories for Robot Control IJCAI 2017
Dynamic-Depth Context Tree Weighting NIPS 2017
Weighted Double Q-learning IJCAI 2017
Multi-Task Deep Reinforcement Learning for Continuous Action Control IJCAI 2017
Improving Reinforcement Learning with Confidence-Based Demonstrations IJCAI 2017
End-to-end optimization of goal-driven and visually grounded dialogue systems IJCAI 2017
Count-Based Exploration in Feature Space for Reinforcement Learning IJCAI 2017
Learning Sparse Representations in Reinforcement Learning with Sparse Coding IJCAI 2017
Efficient Reinforcement Learning with Hierarchies of Machines by Leveraging Internal Transitions IJCAI 2017
Reinforcement Learning under Model Mismatch NIPS 2017
Safe Model-based Reinforcement Learning with Stability Guarantees NIPS 2017
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning ICML 2017
Learning in POMDPs with Monte Carlo Tree Search ICML 2017
FeUdal Networks for Hierarchical Reinforcement Learning ICML 2017
Predictive-State Decoders: Encoding the Future into Recurrent Networks NIPS 2017
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach NIPS 2017
Collaborative Deep Reinforcement Learning for Joint Object Search CVPR 2017
Distral: Robust multitask reinforcement learning NIPS 2017