The temporal Credit Assignment Problem (CAP) is a well-known and challenging task in AI. While Reinforcement Learning (RL), especially Deep RL, works well when immediate rewards are available, it can fail when only delayed rewards are available or when the reward function is noisy. In this work, we propose delegating the CAP to a Neural Network-based algorithm named InferNet that explicitly learns to infer the immediate rewards from the delayed rewards. The effectiveness of InferNet was evaluated on two online RL tasks: a simple GridWorld and 40 Atari games; and two offline RL tasks: GridWorld and a real-life Sepsis treatment task. For all tasks, the effectiveness of using the InferNet inferred rewards is compared against the immediate and the delayed rewards with two settings: with noisy rewards and without noise. Overall, our results show that the effectiveness of InferNet is robust against noisy reward functions and is an effective add-on mechanism for solving temporal CAP in a wide range of RL tasks, from classic RL simulation environments to a real-world RL problem and for both online and offline learning.
When Denser Credit Is Not Enough: Evidence-Calibrated Policy Optimization for Long-Horizon LLM Agent Training
X-LANCE Lab, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China（X-LANCE实验室，计算机科学学院，上海交通大学，上海，中国）；Faculty of Electronic and Information Engineering, Xi’an Jiaotong University（电子与信息工程学院，西安交通大学）
Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning
The Swiss AI Lab IDSIA, USI & SUPSI（瑞士IDSIA人工智能实验室、瑞士意大利语区大学、瑞士南部应用科学大学）；University of Alberta（阿尔伯塔大学）；Alberta Machine Intelligence Institute（阿尔伯塔机器智能研究所）；AI Initiative, King Abdullah University of Science and Technology（阿卜杜拉国王科技大学人工智能计划）
Agent-Temporal Credit Assignment for Optimal Policy Preservation in Sparse Multi-Agent Reinforcement Learning
Tata Consultancy Services（塔塔咨询服务公司）；Birla Institute of Technology and Science, Goa（果阿贝拉理工学院）；University of Edinburgh（爱丁堡大学）；University of Manchester（曼彻斯特大学）；Indian Institute of Technology, Bombay（印度理工学院孟买分校）
STAS: Spatial-Temporal Return Decomposition for Multi-agent Reinforcement Learning
Renmin University of China（中国人民大学）；Peking University（北京大学）；King’s College London（伦敦国王学院）；National Key Laboratory of General Artificial Intelligence, BIGAI（通用人工智能全国重点实验室）
Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach
Eindhoven University of Technology（埃因霍温理工大学）；University of California San Diego（加州大学圣迭戈分校）；University College London（伦敦大学学院）；University of Liverpool（利物浦大学）
HISR: Hindsight Information Modulated Segmental Process Rewards For Multi-turn Agentic Reinforcement Learning
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空航天信息研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Tencent Hunyuan（腾讯文脉）；School of Computer Science and Technology, Tianjin University（天津大学计算机科学与技术学院）
RREDCoT: Segment-Level Reward Redistribution for Reasoning Models
ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria（林茨ELLIS单元和LIT人工智能实验室，机器学习研究所，林茨约瑟夫·冯·拉格纳大学，奥地利）；Cognizant AI Lab, San Francisco, USA（认知人工智能实验室，美国旧金山）；NXAI GmbH, Linz, Austria（NXAI公司，奥地利林茨）
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；The Swiss AI Lab IDSIA/USI/SUPSI（瑞士IDSIA/USI/SUPSI人工智能实验室）；The University of Liverpool（利物浦大学）；University of Oxford（牛津大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；NNAISENSE（NNAISENSE公司）
