Unsupervised object-centric representation (OCR) learning has recently drawn attention as a new paradigm of visual representation. This is because of its potential of being an effective pre-training technique for various downstream tasks in terms of sample efficiency, systematic generalization, and reasoning. Although image-based reinforcement learning (RL) is one of the most important and thus frequently mentioned such downstream tasks, the benefit in RL has surprisingly not been investigated systematically thus far. Instead, most of the evaluations have focused on rather indirect metrics such as segmentation quality and object property prediction accuracy. In this paper, we investigate the effectiveness of OCR pre-training for image-based reinforcement learning via empirical experiments. For systematic evaluation, we introduce a simple object-centric visual RL benchmark and conduct experiments to answer questions such as ``Does OCR pre-training improve performance on object-centric tasks?'' and ``Can OCR pre-training help with out-of-distribution generalization?''. Our results provide empirical evidence for valuable insights into the effectiveness of OCR pre-training for RL and the potential limitations of its use in certain scenarios. Additionally, this study also examines the critical aspects of incorporating OCR pre-training in RL, including performance in a visually complex environment and the appropriate pooling layer to aggregate the object representations.
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）
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。
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）
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）
本文提出 ReBis，通过 Transformer 隐式建模动态并结合互模拟损失与非对称重构损失，在稀疏奖励下学习以控制为中心的图像强化学习表示，并在 Atari 和 DeepMind Control Suit 上验证了其优越性。
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）
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）
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）
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）
Computer Science Department, TU Darmstadt（图宾根大学计算机科学系）；National Research Center for Applied Cybersecurity Darmstadt（德意志应用网络安全国家研究中心）；Hessian Center for Artificial Intelligence (hessian.AI)（黑森人工智能中心）；Centre for Cognitive Science, TU Darmstadt（图宾根大学认知科学中心）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
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）
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）
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）
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）
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）
