arXiv:2607. 12861v1 Announce Type: cross Abstract: Multi-agent Reinforcement Learning (MARL) holds great potential for robot swarms, but the black-box nature of neural policies complicates strategic analysis, limiting multi-robot applications.
Multi-agent Reinforcement Learning (MARL) holds great potential for robot swarms, but the black-box nature of neural policies complicates strategic analysis, limiting multi-robot applications. Furthermore, complex swarm behaviors can surprisingly emerge from simple rewards without explicit aggregation incentives.
arXiv:2608. 20016v1 Announce Type: cross Abstract: Reputation is widely recognized as a key mechanism for sustaining cooperation.
arXiv:2608. 04232v1 Announce Type: new Abstract: Biological systems must regulate competing needs under limited perceptual bandwidth, where sharpening one estimate costs the capacity to sharpen the others.
arXiv:2507.11482v5 Announce Type: replace Abstract: Artificial learning systems are graduating from passive learners to increasingly autonomous agents, lending pragmatic urgency to the question of wh...
arXiv:2607. 14574v1 Announce Type: new Abstract: Collective problem solving often requires that group members consider the tradeoff between exploitation of known solutions and exploration for new ones, where information of known solutions can be disseminated among individual members through communication networks.
arXiv:2505. 15998v4 Announce Type: replace Abstract: We present a curiosity-driven AI scientist method for discovering system-level dynamics in Flow-Lenia, a continuous cellular automaton (CA) with mass conservation and parameter localization.
arXiv:2607. 18589v1 Announce Type: cross Abstract: Reinforcement learning is conventionally divided into model-based and model-free methods.
arXiv:2510. 14907v2 Announce Type: replace-cross Abstract: We extend the study of learning in games to dynamics that exhibit non-asymptotic stability.
arXiv:2607. 19232v1 Announce Type: new Abstract: Hierarchical Reinforcement Learning (HRL) intends to separate strategic planning from primitive execution.
arXiv:2606. 20485v1 Announce Type: cross Abstract: This paper develops a general framework for analyzing multi-agent systems with feedback loops between agents actions and collective observations.
arXiv:2601. 17454v2 Announce Type: replace-cross Abstract: Centralized value learning underlies a broad class of multi-agent reinforcement learning methods, but its claimed advantage is typically evaluated in settings that confound coordination structure with function approximation and partial observability.