policy learning

PACE: Improving Prompt with Actor-Critic Editing for Large Language Model ACL 2024 PACER+: On-Demand Pedestrian Animation Controller in Driving Scenarios CVPR 2024 Parallelizing Model-based Reinforcement Learning Over the Sequence Length NeurIPS 2024 Pearl: A Production-Ready Reinforcement Learning Agent JMLR 2024 Peer Learning: Learning Complex Policies in Groups from Scratch via Action Recommendations AAAI 2024 Planning Like Human: A Dual-process Framework for Dialogue Planning ACL 2024 Point Cloud Matters: Rethinking the Impact of Different Observation Spaces on Robot Learning NeurIPS 2024 Policy Learning for Balancing Short-Term and Long-Term Rewards ICML 2024 Policy Learning for Localized Interventions from Observational Data AISTATS 2024 Policy Learning from Tutorial Books via Understanding, Rehearsing and Introspecting NeurIPS 2024 Positivity-free Policy Learning with Observational Data AISTATS 2024 Preference-Guided Reflective Sampling for Aligning Language Models EMNLP 2024 Pre-trained Visual Dynamics Representations for Efficient Policy Learning ECCV 2024 Principal-Agent Reward Shaping in MDPs AAAI 2024 Prospective Side Information for Latent MDPs ICML 2024 Provably and Practically Efficient Adversarial Imitation Learning with General Function Approximation NeurIPS 2024 Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation NeurIPS 2024 PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning IJCAI 2024 RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning NeurIPS 2024 Rating-Based Reinforcement Learning AAAI 2024 Recent Advancements in Inverse Reinforcement Learning AAAI 2024 Reflect-RL: Two-Player Online RL Fine-Tuning for LMs ACL 2024 Reinforcement Learning from Reachability Specifications: PAC Guarantees with Expected Conditional Distance ICML 2024 Rethinking Inverse Reinforcement Learning: from Data Alignment to Task Alignment NeurIPS 2024 Retrieval-Augmented Embodied Agents CVPR 2024