Reinforcement Learning
Reinforcement learning (RL) focuses on training agents to make optimal decisions in an environment by learning through trial and error, aiming to maximize cumulative rewards. Current research emphasizes improving RL's efficiency and robustness, particularly in areas like human-in-the-loop training (e.g., using human feedback to refine models), handling uncertainty and sparse rewards, and scaling to complex tasks (e.g., robotics, autonomous driving). Prominent approaches involve various policy gradient methods, Monte Carlo Tree Search, and the integration of large language models for improved decision-making and task decomposition. These advancements are driving progress in diverse fields, including robotics, game playing, and the development of more human-aligned AI systems.
Papers
Hamilton-Jacobi Reachability in Reinforcement Learning: A Survey
Milan Ganai, Sicun Gao, Sylvia Herbert
Optimal Defender Strategies for CAGE-2 using Causal Modeling and Tree Search
Kim Hammar, Neil Dhir, Rolf Stadler
Learning Coordinated Maneuver in Adversarial Environments
Zechen Hu, Manshi Limbu, Daigo Shishika, Xuesu Xiao, Xuan Wang
ASTPrompter: Weakly Supervised Automated Language Model Red-Teaming to Identify Likely Toxic Prompts
Amelia F. Hardy, Houjun Liu, Bernard Lange, Mykel J. Kochenderfer
Constrained Intrinsic Motivation for Reinforcement Learning
Xiang Zheng, Xingjun Ma, Chao Shen, Cong Wang
New Desiderata for Direct Preference Optimization
Xiangkun Hu, Tong He, David Wipf
Communication-Aware Reinforcement Learning for Cooperative Adaptive Cruise Control
Sicong Jiang, Seongjin Choi, Lijun Sun
Deep Attention Driven Reinforcement Learning (DAD-RL) for Autonomous Vehicle Decision-Making in Dynamic Environment
Jayabrata Chowdhury, Venkataramanan Shivaraman, Sumit Dangi, Suresh Sundaram, P. B. Sujit
PID Accelerated Temporal Difference Algorithms
Mark Bedaywi, Amin Rakhsha, Amir-massoud Farahmand
MetaUrban: A Simulation Platform for Embodied AI in Urban Spaces
Wayne Wu, Honglin He, Yiran Wang, Chenda Duan, Jack He, Zhizheng Liu, Quanyi Li, Bolei Zhou
A Review of Nine Physics Engines for Reinforcement Learning Research
Michael Kaup, Cornelius Wolff, Hyerim Hwang, Julius Mayer, Elia Bruni
A Cantor-Kantorovich Metric Between Markov Decision Processes with Application to Transfer Learning
Adrien Banse, Venkatraman Renganathan, Raphaël M. Jungers
Gradient Boosting Reinforcement Learning
Benjamin Fuhrer, Chen Tessler, Gal Dalal
PrefCLM: Enhancing Preference-based Reinforcement Learning with Crowdsourced Large Language Models
Ruiqi Wang, Dezhong Zhao, Ziqin Yuan, Ike Obi, Byung-Cheol Min
Learning In-Hand Translation Using Tactile Skin With Shear and Normal Force Sensing
Jessica Yin, Haozhi Qi, Jitendra Malik, James Pikul, Mark Yim, Tess Hellebrekers
Reinforcement Learning of Adaptive Acquisition Policies for Inverse Problems
Gianluigi Silvestri, Fabio Valerio Massoli, Tribhuvanesh Orekondy, Afshin Abdi, Arash Behboodi
BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark
Nikita Chernyadev, Nicholas Backshall, Xiao Ma, Yunfan Lu, Younggyo Seo, Stephen James
Continuous Control with Coarse-to-fine Reinforcement Learning
Younggyo Seo, Jafar Uruç, Stephen James
Real-time system optimal traffic routing under uncertainties -- Can physics models boost reinforcement learning?
Zemian Ke, Qiling Zou, Jiachao Liu, Sean Qian