Reinforcement Learning

Learning Belief Representations for Partially Observable Deep RL ICML 2023 Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning NeurIPS 2023 Learning Compiler Pass Orders using Coreset and Normalized Value Prediction ICML 2023 Learning Compositional Tasks from Language Instructions AAAI 2023 Learning Control by Iterative Inversion ICML 2023 Learning Control Policies for Stochastic Systems with Reach-Avoid Guarantees AAAI 2023 Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning NeurIPS 2023 Learning Efficient Abstract Planning Models that Choose What to Predict CoRL 2023 Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy Gradient AAAI 2023 Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware RSS 2023 Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees ICML 2023 Learning Foresightful Dense Visual Affordance for Deformable Object Manipulation ICCV 2023 Learning-Free Grasping of Unknown Objects Using Hidden Superquadrics RSS 2023 Learning from Active Human Involvement through Proxy Value Propagation NeurIPS 2023 Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning AAAI 2023 Learning from Visual Observation via Offline Pretrained State-to-Go Transformer NeurIPS 2023 Learning Generalizable Agents via Saliency-guided Features Decorrelation NeurIPS 2023 Learning Generalizable Batch Active Learning Strategies via Deep Q-networks (Student Abstract) AAAI 2023 Learning Generalizable Manipulation Policies with Object-Centric 3D Representations CoRL 2023 Learning Good State and Action Representations for Markov Decision Process via Tensor Decomposition JMLR 2023 Learning Human Contribution Preferences in Collaborative Human-Robot Tasks CoRL 2023 Learning Human Dynamics in Autonomous Driving Scenarios ICCV 2023 Learning Human-to-Robot Handovers From Point Clouds CVPR 2023 Learning in POMDPs is Sample-Efficient with Hindsight Observability ICML 2023 Learning Intuitive Policies Using Action Features ICML 2023