arXiv Machine Learning

Q-based Variational Inverse Reinforcement Learning

arXiv:2608. 16888v1 Announce Type: new Abstract: The development of safe and beneficial AI requires that systems can learn and act in accordance with human preferences.

arXiv AI

Jul 3

Trust Region Inverse Reinforcement Learning: Explicit Dual Ascent using Local Policy Updates

arXiv:2605. 11020v2 Announce Type: replace-cross Abstract: Inverse reinforcement learning (IRL) is typically formulated as maximizing entropy subject to matching the distribution of expert trajectories.

By Anish Diwan, Davide Tateo, Christopher E. Mower, Haitham Bou-Ammar, Jan Peters, Oleg Arenz

reinforcement-learning benchmarks safety

arXiv AI

Jul 10

Multi-Modal, Multi-Environment Machine Teaching for Robust Reward Learning

arXiv:2607. 08647v1 Announce Type: cross Abstract: As autonomous agents are increasingly deployed across diverse operational contexts, aligning their behavior with human intent demands reward functions that remain robust to such changes rather than overfitting to any single environment.

By Ali Larian, Qian Lin, Chang Zong Wu, Daniel S. Brown

agents reinforcement-learning multimodal

arXiv Machine Learning

Aug 10

Game-Theoretic Inverse Reinforcement Learning for Modeling Competitive Human Driving: A Cut-in Prediction Study

arXiv:2608. 06445v1 Announce Type: cross Abstract: Capturing the strategic decision-making inherent in competitive human driving is critical for autonomous vehicle safety and traffic simulation.

By Yu Song

agents reinforcement-learning benchmarks safety

arXiv AI

Jul 24

Expert Behavior Prior Reinforcement Learning

arXiv:2607. 21302v1 Announce Type: new Abstract: Behavior prior reinforcement learning (BPRL) has emerged as a promising paradigm to improve sample efficiency in online reinforcement learning (RL) by leveraging policy priors derived from offline demonstrations.

By Gong Gao, Weidong Zhao, Xianhui Liu, Ning Jia

agents reinforcement-learning robotics benchmarks

arXiv AI

Jul 3

Playing 20 Question Game with Policy-Based Reinforcement Learning

arXiv:1808. 07645v5 Announce Type: replace-cross Abstract: The 20 Questions (Q20) game is a well known game which encourages deductive reasoning and creativity.

By Huang Hu, Xianchao Wu, Bingfeng Luo, Chongyang Tao, Can Xu, Wei Wu, Zhan Chen

rag agents reinforcement-learning

arXiv AI

Jun 30

TERC: A Transfer Entropy Redundancy Criterion for State Variable Selection in Reinforcement Learning

arXiv:2401. 11512v2 Announce Type: replace-cross Abstract: Identifying the most suitable variables to represent the state is a fundamental challenge in Reinforcement Learning (RL).

By Charles Westphal, Stephen Hailes, Mirco Musolesi

agents reinforcement-learning benchmarks

arXiv AI

Jun 17

Reversal Q-Learning

arXiv:2606. 17551v1 Announce Type: cross Abstract: Iterative generative modeling techniques, such as flow matching, provide powerful tools to model complex behaviors for effective offline reinforcement learning (RL).

By Aditya Oberai, Seohong Park, Sergey Levine

diffusion reinforcement-learning robotics benchmarks safety

arXiv Machine Learning

Jun 2

Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards

arXiv:2606. 02194v1 Announce Type: new Abstract: Distilling expert demonstration data into large generative models using behavioral cloning is a scalable approach to learning capable policies for robotic control, particularly for dexterous manipulation.

By Christian Scherer, Joe Watson, Theo Gruner, Daniel Palenicek, Ingmar Posner, Jan Peters

reinforcement-learning robotics fine-tuning

arXiv AI

Jun 2

ExpertGen: Scalable Sim-to-Real Expert Policy Learning from Imperfect Behavior Priors

arXiv:2603. 15956v3 Announce Type: replace-cross Abstract: Learning generalizable and robust behavior cloning policies requires large volumes of high-quality robotics data.

By Zifan Xu, Ran Gong, Maria Vittoria Minniti, Kausik Sivakumar, Ahmet Salih Gundogdu, Eric Rosen, Riedana Yan, Tushar Kusnur, Zixing Wang, Di Deng, Peter Stone, Xiaohan Zhang, Karl Schmeckpeper

llms diffusion reinforcement-learning robotics benchmarks

arXiv AI

Jul 21

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning

arXiv:2607. 17760v1 Announce Type: cross Abstract: Inverse reinforcement learning (IRL) provides a powerful framework for learning from demonstrations.

By Ziyi Liu, Grace Zhang

agents reinforcement-learning robotics

arXiv AI

Jul 7

rePIRL: Learn PRM with Inverse RL for LLM Reasoning

arXiv:2602. 07832v3 Announce Type: replace-cross Abstract: Process rewards have been widely used in deep reinforcement learning to improve training efficiency, reduce variance, and prevent reward hacking.

By Xian Wu, Kaijie Zhu, Ying Zhang, Lun Wang, Wenbo Guo

llms reinforcement-learning benchmarks

arXiv AI

Jul 10

Feedback Manipulation Regularization: Enabling Offline Agent Alignment for Imitation Learning

arXiv:2607. 07859v1 Announce Type: new Abstract: Reinforcement learning (RL) research has increasingly shifted focus towards alignment, ensuring agents learn behaviors adhering to human values.

By Benjamin Poole, Minwoo Lee

agents reinforcement-learning robotics safety

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