Towards Control-Centric Representations in Reinforcement Learning from Images
Beijing Institute of Electronic System Engineering（北京电子系统工程研究所）；Peking University（北京大学）；Beijing Institute of Technology（北京理工大学）；University of the Sunshine Coast（阳光海岸大学）
Image-based Reinforcement Learning is a practical yet challenging task. A major hurdle lies in extracting control-centric representations while disregarding irrelevant information. While approaches that follow the bisimulation principle exhibit the potential in learning state representations to address this issue, they still grapple with the limited expressive capacity of latent dynamics and the inadaptability to sparse reward environments. To address these limitations, we introduce ReBis, which aims to capture control-centric information by integrating reward-free control information alongside reward-specific knowledge. ReBis utilizes a transformer architecture to implicitly model the dynamics and incorporates block-wise masking to eliminate spatiotemporal redundancy. Moreover, ReBis combines bisimulation-based loss with asymmetric reconstruction loss to prevent feature collapse in environments with sparse rewards. Empirical studies on two large benchmarks, including Atari games and DeepMind Control Suit, demonstrate that ReBis has superior performance compared to existing methods, proving its effectiveness.
Agent-Centric Representations for Multi-Agent Reinforcement Learning
DeepMind；Google Research（谷歌研究院）
以对象为中心的表示最近在处理关系推理任务方面取得了重大进展。通过在神经架构中构建强的以对象为中心的归纳偏差，最近的努力已经提高了这些问题的机器学习算法的泛化和数据效率。涉及关系推理的一个问题类别仍然未得到充分探讨，那就是多智能体强化学习（MARL）。在这里，我们调查是否以对象为中心的表示也是有益的，在充分合作的MARL设置。具体来说，我们研究了两种方法，将代理为中心的归纳偏见到我们的RL算法：1。引入一个以代理为中心的注意力模块，并在代理之间建立显式连接2。添加以代理为中心的无监督预测目标（即不使用动作标签），用作MARL的辅助损失，或作为预训练步骤的基础。我们在Google Research Football环境和DeepMind Lab 2D上评估了这些方法。经验上，以主体为中心的表示学习导致主体之间更复杂的合作策略的出现，以及增强的样本效率和泛化能力。
Learning Interactive World Model for Object-Centric Reinforcement Learning
University of California San Diego（加州大学圣地亚哥分校）；Mohamed bin Zayed University of Artificial Intelligence（马尔代夫穆罕默德·本·扎耶德人工智能大学）；University of Amsterdam（阿姆斯特丹大学）
Deep Reinforcement Learning via Object-Centric Attention
Technical University of Darmstadt（达姆施塔特工业大学）；Hessian Center for Artificial Intelligence (hessian.AI)（黑森人工智能中心）；Sorbonne Université（索邦大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Centre for Cognitive Science, Darmstadt, Germany（达姆施塔特认知科学中心）
Technical University of Darmstadt（达姆施塔特工业大学）；Hessian Center for Artificial Intelligence (hessian.AI)（黑森人工智能中心）；Centre for Cognitive Science of Darmstadt（达姆施塔特认知科学中心）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
Local Policy Optimization for Trajectory-Centric Reinforcement Learning
UTA Research Institute, University of Texas at Arlington（德克萨斯大学阿灵顿分校UTA研究所）；Mitsubishi Electric Research Laboratories (MERL)（三菱电机研究实验室（MERL））；University of Texas at Arlington（德克萨斯大学阿灵顿分校）
Human-Centric Traffic Signal Control for Equity: A Multi-Agent Action Branching Deep Reinforcement Learning Approach
Department of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, VIC 3010, Australia（工程与信息技术学院基础设施工程系，墨尔本大学）
Human-Centric Aware UAV Trajectory Planning in Search and Rescue Missions Employing Multi-Objective Reinforcement Learning with AHP and Similarity-Based Experience Replay
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg（卢森堡大学安全、可靠性与信任跨学科中心）；University of Luxembourg（卢森堡大学）
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions
Peking University（北京大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；National University of Singapore（新加坡国立大学）；Nankai University（南开大学）；Tongji University（同济大学）；Nanjing University（南京大学）；University of Wollongong（沃林根大学）；Shanghai Jiao Tong University（上海交通大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
TempoNet: Slack-Quantized Transformer-Guided Reinforcement Scheduler for Adaptive Deadline-Centric Real-Time Dispatchs
University of Macau（澳门大学）；Tsinghua University（清华大学）；Northeast Normal University（东北师范大学）；Shanghai AI Laboratory（上海人工智能实验室）；The Australian National University（澳大利亚国立大学）；Peking University（北京大学）；Harbin University of Science and Technology（哈尔滨理工大学）；Renmin University of China（中国人民大学）；Adelaide University（阿德莱德大学）
OpenRL: A Unified Reinforcement Learning Framework
Paradigm Inc.（第四范式）；Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；School of Data Science, Fudan University（复旦大学大数据学院）；Beijing University of Posts and Telecommunications（北京邮电大学）
Spatio-temporal dual-stage hypergraph MARL for human-centric multimodal corridor traffic signal control
Department of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, VIC 3010, Australia（工程与信息技术学院基础设施工程系，墨尔本大学）
Slot-MPC: Goal-Conditioned Model Predictive Control with Object-Centric Representations
Autonomous Intelligent Systems（自主智能系统）；Computer Science Institute VI（计算机科学研究所VI）；Intelligent Systems and Robotics（智能系统与机器人）；Center for Robotics（机器人中心）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔人工智能学习与智能研究所）；University of Bonn, Germany（波恩大学，德国）
Discovering Object-Centric Generalized Value Functions From Pixels
École de technologie supérieure（高等技术学院）；Mila-Quebec AI Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；McGill University（麦吉尔大学）；CIFAR AI Chair（加拿大高等研究院人工智能讲席）；DeepMind（深度思维）
OCALM: Object-Centric Assessment with Language Models
LMU Munich（慕尼黑大学）；Munich Center of Machine Learning (MCML)（慕尼黑机器学习中心）；Technical University of Darmstadt（达姆施塔特工业大学）；Hessian Center for Artificial Intelligence (hessian.AI)（黑森人工智能中心）；National Research Center for Applied Cybersecurity (Athene)（国家应用网络安全研究中心）；Centre for Cognitive Science of Darmstadt（达姆施塔特认知科学中心）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks and Autoregressive Policy Decomposition
Czech Technical University in Prague（布拉格捷克技术大学）；Artificial Intelligence Center, Department of Computer Science, Faculty of Electrical Engineering（电气工程学院计算机科学系人工智能中心）
Dept. of Computer Science University of Minnesota, Twin Cities（计算机科学系明尼苏达大学双城分校）；Depts. of Computer Science and Psychology University of Minnesota, Twin Cities（计算机科学与心理学系明尼苏达大学双城分校）
