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
Hitting the Gym: Reinforcement Learning Control of Exercise-Strengthened Biohybrid Robots in Simulation
Saul Schaffer, Hima Hrithik Pamu, Victoria A. Webster-Wood
Adaptive Traffic Signal Control Using Reinforcement Learning
Muhammad Tahir Rafique, Ahmed Mustafa, Hasan Sajid
An Extremely Data-efficient and Generative LLM-based Reinforcement Learning Agent for Recommenders
Shuang Feng, Grace Feng
Structural Optimization of Lightweight Bipedal Robot via SERL
Yi Cheng, Chenxi Han, Yuheng Min, Linqi Ye, Houde Liu, Hang Liu
Skills Regularized Task Decomposition for Multi-task Offline Reinforcement Learning
Minjong Yoo, Sangwoo Cho, Honguk Woo
Simultaneous Training of First- and Second-Order Optimizers in Population-Based Reinforcement Learning
Felix Pfeiffer, Shahram Eivazi
What makes math problems hard for reinforcement learning: a case study
Ali Shehper, Anibal M. Medina-Mardones, Bartłomiej Lewandowski, Angus Gruen, Piotr Kucharski, Sergei Gukov
Evaluating the Impact of Multiple DER Aggregators on Wholesale Energy Markets: A Hybrid Mean Field Approach
Jun He, Andrew L. Liu
No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery
Alexander Rutherford, Michael Beukman, Timon Willi, Bruno Lacerda, Nick Hawes, Jakob Foerster
MiWaves Reinforcement Learning Algorithm
Susobhan Ghosh, Yongyi Guo, Pei-Yao Hung, Lara Coughlin, Erin Bonar, Inbal Nahum-Shani, Maureen Walton, Susan Murphy
Inverse-Q*: Token Level Reinforcement Learning for Aligning Large Language Models Without Preference Data
Han Xia, Songyang Gao, Qiming Ge, Zhiheng Xi, Qi Zhang, Xuanjing Huang
Learning Robust Reward Machines from Noisy Labels
Roko Parac, Lorenzo Nodari, Leo Ardon, Daniel Furelos-Blanco, Federico Cerutti, Alessandra Russo
Dynamic operator management in meta-heuristics using reinforcement learning: an application to permutation flowshop scheduling problems
Maryam Karimi Mamaghan, Mehrdad Mohammadi, Wout Dullaert, Daniele Vigo, Amir Pirayesh
MA-CDMR: An Intelligent Cross-domain Multicast Routing Method based on Multiagent Deep Reinforcement Learning in Multi-domain SDWN
Miao Ye, Hongwen Hu, Xiaoli Wang, Yuping Wang, Yong Wang, Wen Peng, Jihao Zheng
Benchmarking Reinforcement Learning Methods for Dexterous Robotic Manipulation with a Three-Fingered Gripper
Elizabeth Cutler, Yuning Xing, Tony Cui, Brendan Zhou, Koen van Rijnsoever, Ben Hart, David Valencia, Lee Violet C. Ong, Trevor Gee, Minas Liarokapis, Henry Williams
Rethinking State Disentanglement in Causal Reinforcement Learning
Haiyao Cao, Zhen Zhang, Panpan Cai, Yuhang Liu, Jinan Zou, Ehsan Abbasnejad, Biwei Huang, Mingming Gong, Anton van den Hengel, Javen Qinfeng Shi
Data Augmentation for Continual RL via Adversarial Gradient Episodic Memory
Sihao Wu, Xingyu Zhao, Xiaowei Huang