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Deep RL

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Papers

Representation Learning in Deep RL via Discrete Information Bottleneck AISTATS 2023
Model-Based Uncertainty in Value Functions AISTATS 2023
Provably Efficient Model-Free Algorithms for Non-stationary CMDPs AISTATS 2023
Reinforcement Learning for Adaptive Mesh Refinement AISTATS 2023
Neural Simulated Annealing AISTATS 2023
Provably Efficient Reinforcement Learning via Surprise Bound AISTATS 2023
Byzantine-Robust Online and Offline Distributed Reinforcement Learning AISTATS 2023
Replicable Reinforcement Learning NIPS 2023
A finite-sample analysis of multi-step temporal difference estimates L4DC 2023
Provably Efficient Model-free RL in Leader-Follower MDP with Linear Function Approximation L4DC 2023
Robot Parkour Learning CORL 2023
HACMan: Learning Hybrid Actor-Critic Maps for 6D Non-Prehensile Manipulation CORL 2023
Learning Realistic Traffic Agents in Closed-loop CORL 2023
Contrastive Value Learning: Implicit Models for Simple Offline RL CORL 2023
Robust Reinforcement Learning in Continuous Control Tasks with Uncertainty Set Regularization CORL 2023
Context-Aware Deep Reinforcement Learning for Autonomous Robotic Navigation in Unknown Area CORL 2023
Sample-Efficient Preference-based Reinforcement Learning with Dynamics Aware Rewards CORL 2023
REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation CORL 2023
Complex Video Action Reasoning via Learnable Markov Logic Network CVPR 2022
Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning NIPS 2022
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning ICML 2022
On the Convergence Rate of Off-Policy Policy Optimization Methods with Density-Ratio Correction AISTATS 2022
Polynomial Time Reinforcement Learning in Factored State MDPs with Linear Value Functions AISTATS 2022
Controllable Text Simplification with Deep Reinforcement Learning IJCNLP 2022
Dynamic Dialogue Policy for Continual Reinforcement Learning COLING 2022