Assessing the systemic effects of uncertainty that arises from agents' partial observation of the true states of the world is critical for understanding a wide range of scenarios. Yet, previous modeling work on agent learning and decision-making either lacks a systematic way to describe this source of uncertainty or puts the focus on obtaining optimal policies using complex models of the world that would impose an unrealistically high cognitive demand on real agents. In this work we aim to efficiently describe the emergent behavior of biologically plausible and parsimonious learning agents faced with partially observable worlds. Therefore we derive and present deterministic reinforcement learning dynamics where the agents observe the true state of the environment only partially. We showcase the broad applicability of our dynamics across different classes of partially observable agent-environment systems. We find that partial observability creates unintuitive benefits in a number of specific contexts, pointing the way to further research on a general understanding of such effects. For instance, partially observant agents can learn better outcomes faster, in a more stable way and even overcome social dilemmas. Furthermore, our method allows the application of dynamical systems theory to partially observable multiagent leaning. In this regard we find the emergence of catastrophic limit cycles, a critical slowing down of the learning processes between reward regimes and the separation of the learning dynamics into fast and slow directions, all caused by partial observability. Therefore, the presented dynamics have the potential to become a formal, yet practical, lightweight and robust tool for researchers in biology, social science and machine learning to systematically investigate the effects of interacting partially observant agents.
RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Interactive Environmental Learning in Physical Embodied Systems
FNii-Shenzhen（FNii-深圳）；SSE, CUHK-Shenzhen（SSE，CUHK-深圳）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；The University of Hong Kong（香港大学）；National University of Singapore（新加坡国立大学）；The Chinese University of Hong Kong（香港中文大学）；Ising AI
Prompt Injection Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching
Head of AI, Tesisquare Member, Open Voice Interoperability Initiative Linux Foundation AI & Data Torino, Italy（AI负责人，Tesisquare成员，开放语音互操作性倡议Linux基金会AI与数据部门，意大利托里诺）；Principal, Conversational Technologies Member, Open Voice Interoperability Initiative Linux Foundation AI & Data Plymouth Meeting, PA, USA（首席科学家，对话技术成员，开放语音互操作性倡议Linux基金会AI与数据部门，美国普利茅斯会议）
Reward Engineering for Spatial Epidemic Simulations: A Reinforcement Learning Platform for Individual Behavioral Learning
Department of Computer Science University of British Columbia（计算机科学系，不列颠哥伦比亚大学）；Department of Mathematics and Institute of Applied Mathematics University of British Columbia（数学系和应用数学研究所，不列颠哥伦比亚大学）
Learning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning
Northwestern Polytechnical University（西北工业大学）；Harbin Engineering University（哈尔滨工程大学）；Hong Kong University of Science and Technology（香港科技大学）；Xidian University（西安电子科技大学）；Nanyang Technological University（南洋理工大学）
Curriculum-Based Reinforcement Learning for Autonomous UAV Navigation in Unknown Curved Tubular Conduit
DGA Techniques Navales - Direction Générale de l’Armement（法国国防总局海军技术部）；LIRMM, TETIS, CNRS, Université de Montpellier Paul-Valéry（蒙彼利埃大学保罗-瓦莱里耶大学）；LIS, CNRS, Université de Toulon（土伦大学）
