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
CoMAL: Collaborative Multi-Agent Large Language Models for Mixed-Autonomy Traffic
Huaiyuan Yao, Longchao Da, Vishnu Nandam, Justin Turnau, Zhiwei Liu, Linsey Pang, Hua Wei
MarineGym: Accelerated Training for Underwater Vehicles with High-Fidelity RL Simulation
Shuguang Chu, Zebin Huang, Mingwei Lin, Dejun Li, Ignacio Carlucho
Reward-free World Models for Online Imitation Learning
Shangzhe Li, Zhiao Huang, Hao Su
Sliding Puzzles Gym: A Scalable Benchmark for State Representation in Visual Reinforcement Learning
Bryan L. M. de Oliveira, Murilo L. da Luz, Bruno Brandão, Luana G. B. Martins, Telma W. de L. Soares, Luckeciano C. Melo
Adversarial Inception for Bounded Backdoor Poisoning in Deep Reinforcement Learning
Ethan Rathbun, Christopher Amato, Alina Oprea
RecoveryChaining: Learning Local Recovery Policies for Robust Manipulation
Shivam Vats, Devesh K. Jha, Maxim Likhachev, Oliver Kroemer, Diego Romeres
Approximating Auction Equilibria with Reinforcement Learning
Pranjal Rawat
Guided Reinforcement Learning for Robust Multi-Contact Loco-Manipulation
Jean-Pierre Sleiman, Mayank Mittal, Marco Hutter
Transformer Guided Coevolution: Improved Team Formation in Multiagent Adversarial Games
Pranav Rajbhandari, Prithviraj Dasgupta, Donald Sofge
Integrating Large Language Models and Reinforcement Learning for Non-Linear Reasoning
Yoav Alon, Cristina David
Novelty-based Sample Reuse for Continuous Robotics Control
Ke Duan, Kai Yang, Houde Liu, Xueqian Wang
Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach
Riccardo Poiani, Nicole Nobili, Alberto Maria Metelli, Marcello Restelli
An Evolved Universal Transformer Memory
Edoardo Cetin, Qi Sun, Tianyu Zhao, Yujin Tang
Reinforcement Learning with Euclidean Data Augmentation for State-Based Continuous Control
Jinzhu Luo, Dingyang Chen, Qi Zhang
In-Context Learning Enables Robot Action Prediction in LLMs
Yida Yin, Zekai Wang, Yuvan Sharma, Dantong Niu, Trevor Darrell, Roei Herzig
Neural-based Control for CubeSat Docking Maneuvers
Matteo Stoisa, Federica Paganelli Azza, Luca Romanelli, Mattia Varile
Robust RL with LLM-Driven Data Synthesis and Policy Adaptation for Autonomous Driving
Sihao Wu, Jiaxu Liu, Xiangyu Yin, Guangliang Cheng, Meng Fang, Xingyu Zhao, Xinping Yi, Xiaowei Huang
Dual Action Policy for Robust Sim-to-Real Reinforcement Learning
Ng Wen Zheng Terence, Chen Jianda
EdgeRL: Reinforcement Learning-driven Deep Learning Model Inference Optimization at Edge
Motahare Mounesan, Xiaojie Zhang, Saptarshi Debroy
Reinforcement Learning with LTL and $ω$-Regular Objectives via Optimality-Preserving Translation to Average Rewards
Xuan-Bach Le, Dominik Wagner, Leon Witzman, Alexander Rabinovich, Luke Ong