← Methods
Reinforcement Learning › Methods ›

Deep RL

3861 directly classified papers

Papers per year

Papers

Composing Elementary Discourse Units in Abstractive Summarization ACL 2020
A Machine of Few Words: Interactive Speaker Recognition with Reinforcement Learning INTERSPEECH 2020
Prototypical Q Networks for Automatic Conversational Diagnosis and Few-Shot New Disease Adaption INTERSPEECH 2020
Stochastic Curiosity Exploration for Dialogue Systems INTERSPEECH 2020
Incremental Text to Speech for Neural Sequence-to-Sequence Models Using Reinforcement Learning INTERSPEECH 2020
Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently ICML 2020
Estimating Q(s,s’) with Deep Deterministic Dynamics Gradients ICML 2020
Reasoning Like Human: Hierarchical Reinforcement Learning for Knowledge Graph Reasoning IJCAI 2020
Learning Dialog Policies from Weak Demonstrations ACL 2020
History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms ICML 2020
Fast Adaptation to New Environments via Policy-Dynamics Value Functions ICML 2020
Striving for Simplicity and Performance in Off-Policy DRL: Output Normalization and Non-Uniform Sampling ICML 2020
How Should an Agent Practice? AAAI 2020
Google Research Football: A Novel Reinforcement Learning Environment AAAI 2020
Model and Reinforcement Learning for Markov Games with Risk Preferences AAAI 2020
Task-Completion Dialogue Policy Learning via Monte Carlo Tree Search with Dueling Network EMNLP 2020
Model-based Policy Optimization with Unsupervised Model Adaptation NIPS 2020
A Novel Automated Curriculum Strategy to Solve Hard Sokoban Planning Instances NIPS 2020
Deep Reinforcement and InfoMax Learning NIPS 2020
Adaptive Discretization for Model-Based Reinforcement Learning NIPS 2020
AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control NIPS 2020
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning NIPS 2020
Munchausen Reinforcement Learning NIPS 2020
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards NIPS 2020
Reward-rational (implicit) choice: A unifying formalism for reward learning NIPS 2020