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Deep RL

3861 directly classified papers

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Papers

Sequence Prediction with Unlabeled Data by Reward Function Learning IJCAI 2017
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning NIPS 2017
Hindsight Experience Replay NIPS 2017
Teaching Machines to Describe Images with Natural Language Feedback NIPS 2017
Imagination-Augmented Agents for Deep Reinforcement Learning NIPS 2017
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning NIPS 2017
Runtime Neural Pruning NIPS 2017
Boltzmann Exploration Done Right NIPS 2017
Deep Reinforcement Learning from Human Preferences NIPS 2017
Natural Value Approximators: Learning when to Trust Past Estimates NIPS 2017
Value Prediction Network NIPS 2017
EX2: Exploration with Exemplar Models for Deep Reinforcement Learning NIPS 2017
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning NIPS 2017
Hybrid Reward Architecture for Reinforcement Learning NIPS 2017
Learning Combinatorial Optimization Algorithms over Graphs NIPS 2017
Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback EMNLP 2017
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability ICML 2017
DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning EMNLP 2017
Deep Reinforcement Learning of Dialogue Policies with Less Weight Updates INTERSPEECH 2017
Domain-Independent User Satisfaction Reward Estimation for Dialogue Policy Learning INTERSPEECH 2017
Device Placement Optimization with Reinforcement Learning ICML 2017
Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning ICML 2017
From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood ACL 2017
Semi-Supervised QA with Generative Domain-Adaptive Nets ACL 2017
Value Iteration Networks IJCAI 2017