Reinforcement Learning

Beyond Expected Return: Accounting for Policy Reproducibility When Evaluating Reinforcement Learning Algorithms AAAI 2024 Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement Learning AAAI 2024 Beyond Optimism: Exploration With Partially Observable Rewards NeurIPS 2024 Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies ICLR 2024 Bigger, Regularized, Optimistic: scaling for compute and sample efficient continuous control NeurIPS 2024 BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark CoRL 2024 Bi-Level Motion Imitation for Humanoid Robots CoRL 2024 Bio-inspired Dynamic and Decentralized Online Learning in Uninformed Heterogeneous Multi-Agent Environments IJCAI 2024 Bit_numeval at SemEval-2024 Task 7: Enhance Numerical Sensitivity and Reasoning Completeness for Quantitative Understanding SemEval 2024 Blending Imitation and Reinforcement Learning for Robust Policy Improvement ICLR 2024 Body Transformer: Leveraging Robot Embodiment for Policy Learning CoRL 2024 Boosting Efficiency in Task-Agnostic Exploration through Causal Knowledge IJCAI 2024 Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays ICML 2024 Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance NeurIPS 2024 Bootstrapped Policy Learning for Task-oriented Dialogue through Goal Shaping EMNLP 2024 Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight CoRL 2024 Bounded robustness in reinforcement learning via lexicographic objectives L4DC 2024 Breadth-First Exploration on Adaptive Grid for Reinforcement Learning ICML 2024 Breaking the Barrier: Enhanced Utility and Robustness in Smoothed DRL Agents ICML 2024 Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds JMLR 2024 Bridging Environments and Language with Rendering Functions and Vision-Language Models ICML 2024 Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learning CoRL 2024 Bridging the Sim-to-Real Gap from the Information Bottleneck Perspective CoRL 2024 Bring Your Own (Non-Robust) Algorithm to Solve Robust MDPs by Estimating The Worst Kernel ICML 2024 Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning AAAI 2024