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
A Unified Approach to Multi-task Legged Navigation: Temporal Logic Meets Reinforcement Learning
Jesse Jiang, Samuel Coogan, Ye Zhao
Intercepting Unauthorized Aerial Robots in Controlled Airspace Using Reinforcement Learning
Francisco Giral, Ignacio Gómez, Soledad Le Clainche
Frequency and Generalisation of Periodic Activation Functions in Reinforcement Learning
Augustine N. Mavor-Parker, Matthew J. Sargent, Caswell Barry, Lewis Griffin, Clare Lyle
Powerful and Flexible: Personalized Text-to-Image Generation via Reinforcement Learning
Fanyue Wei, Wei Zeng, Zhenyang Li, Dawei Yin, Lixin Duan, Wen Li
Preference-Guided Reinforcement Learning for Efficient Exploration
Guojian Wang, Faguo Wu, Xiao Zhang, Tianyuan Chen, Xuyang Chen, Lin Zhao
System stabilization with policy optimization on unstable latent manifolds
Steffen W. R. Werner, Benjamin Peherstorfer
Periodic agent-state based Q-learning for POMDPs
Amit Sinha, Matthieu Geist, Aditya Mahajan
Stranger Danger! Identifying and Avoiding Unpredictable Pedestrians in RL-based Social Robot Navigation
Sara Pohland, Alvin Tan, Prabal Dutta, Claire Tomlin
iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvement
Aoyu Pang, Maonan Wang, Man-On Pun, Chung Shue Chen, Xi Xiong
On Bellman equations for continuous-time policy evaluation I: discretization and approximation
Wenlong Mou, Yuhua Zhu
Graph Anomaly Detection with Noisy Labels by Reinforcement Learning
Zhu Wang, Shuang Zhou, Junnan Dong, Chang Yang, Xiao Huang, Shengjie Zhao
An open source Multi-Agent Deep Reinforcement Learning Routing Simulator for satellite networks
Federico Lozano-Cuadra, Mathias D. Thorsager, Israel Leyva-Mayorga, Beatriz Soret
Structural Generalization in Autonomous Cyber Incident Response with Message-Passing Neural Networks and Reinforcement Learning
Jakob Nyberg, Pontus Johnson
Generalizing soft actor-critic algorithms to discrete action spaces
Le Zhang, Yong Gu, Xin Zhao, Yanshuo Zhang, Shu Zhao, Yifei Jin, Xinxin Wu
Learning Velocity-based Humanoid Locomotion: Massively Parallel Learning with Brax and MJX
William Thibault, William Melek, Katja Mombaur
Communication and Control Co-Design in 6G: Sequential Decision-Making with LLMs
Xianfu Chen, Celimuge Wu, Yi Shen, Yusheng Ji, Tsutomu Yoshinaga, Qiang Ni, Charilaos C. Zarakovitis, Honggang Zhang
Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially Observable Environments
Ainur Zhaikhan, Ali H. Sayed