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
Offline-to-Online Multi-Agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration
Hai Zhong, Xun Wang, Zhuoran Li, Longbo Huang
Evolving choice hysteresis in reinforcement learning: comparing the adaptive value of positivity bias and gradual perseveration
Isabelle Hoxha, Leo Sperber, Stefano Palminteri
Toward Finding Strong Pareto Optimal Policies in Multi-Agent Reinforcement Learning
Bang Giang Le, Viet Cuong Ta
Reinforcement Learning the Chromatic Symmetric Function
Gergely Bérczi, Jonas Klüver
PointPatchRL -- Masked Reconstruction Improves Reinforcement Learning on Point Clouds
Balázs Gyenes, Nikolai Franke, Philipp Becker, Gerhard Neumann
SAMG: State-Action-Aware Offline-to-Online Reinforcement Learning with Offline Model Guidance
Liyu Zhang, Haochi Wu, Xu Wan, Quan Kong, Ruilong Deng, Mingyang Sun
Evolutionary Dispersal of Ecological Species via Multi-Agent Deep Reinforcement Learning
Wonhyung Choi, Inkyung Ahn
Towards Reinforcement Learning Controllers for Soft Robots using Learned Environments
Uljad Berdica, Matthew Jackson, Niccolò Enrico Veronese, Jakob Foerster, Perla Maiolino
Multi-UAV Behavior-based Formation with Static and Dynamic Obstacles Avoidance via Reinforcement Learning
Yuqing Xie, Chao Yu, Hongzhi Zang, Feng Gao, Wenhao Tang, Jingyi Huang, Jiayu Chen, Botian Xu, Yi Wu, Yu Wang
Learn 2 Rage: Experiencing The Emotional Roller Coaster That Is Reinforcement Learning
Lachlan Mares, Stefan Podgorski, Ian Reid
SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions
Zizhao Wang, Jiaheng Hu, Caleb Chuck, Stephen Chen, Roberto Martín-Martín, Amy Zhang, Scott Niekum, Peter Stone
Leveraging Skills from Unlabeled Prior Data for Efficient Online Exploration
Max Wilcoxson, Qiyang Li, Kevin Frans, Sergey Levine
SPIRE: Synergistic Planning, Imitation, and Reinforcement Learning for Long-Horizon Manipulation
Zihan Zhou, Animesh Garg, Dieter Fox, Caelan Garrett, Ajay Mandlekar
Adaptive Dense Reward: Understanding the Gap Between Action and Reward Space in Alignment
Yanshi Li, Shaopan Xiong, Gengru Chen, Xiaoyang Li, Yijia Luo, Xingyao Zhang, Yanhui Huang, Xingyuan Bu, Yingshui Tan, Chun Yuan, Jiamang Wang, Wenbo Su, Bo Zheng
Reinforcement Learning under Latent Dynamics: Toward Statistical and Algorithmic Modularity
Philip Amortila, Dylan J. Foster, Nan Jiang, Akshay Krishnamurthy, Zakaria Mhammedi
Learning Versatile Skills with Curriculum Masking
Yao Tang, Zhihui Xie, Zichuan Lin, Deheng Ye, Shuai Li
Optimizing Load Scheduling in Power Grids Using Reinforcement Learning and Markov Decision Processes
Dongwen Luo
Process Supervision-Guided Policy Optimization for Code Generation
Ning Dai, Zheng Wu, Renjie Zheng, Ziyun Wei, Wenlei Shi, Xing Jin, Guanlin Liu, Chen Dun, Liang Huang, Lin Yan
Bridging Swarm Intelligence and Reinforcement Learning
Karthik Soma, Yann Bouteiller, Heiko Hamann, Giovanni Beltrame