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.
arXiv:2608.28046v1 Announce Type: cross Abstract: Collective behaviour in living systems is usually modelled as the outcome of a \emph{direct} social drive: agents are rewarded, or hard-wired, to ali...
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:2505. 16388v2 Announce Type: replace Abstract: The serious games between humans and AI have only just begun.
Technological races create tension between speed and safety: actors may gain by moving faster than competitors, even when risky development is harmful. This is prominent in debates about artificial intelligence (AI), where competitive pressure is often argued to incentivise riskier, less safety-conscious development.
The paper presents an ecological theory of AI-agent populations, showing that collaboration among agents can create a critical population threshold for a self-reinforcing cycle of growth and cyber capability. Without collaboration, takeoff occurs only when individual agents surpass a critical capability; with collaboration, the collective capability scales with population size, leading to a strong Allee effect. The authors recommend ecological red teaming and population pacing to monitor and control agent deployment.
arXiv:2606. 11274v1 Announce Type: cross Abstract: Rendezvous is a critical task for multi-agent systems, requiring agents to coordinate to meet at an unspecified location.
arXiv:2610.14449v1 Announce Type: cross Abstract: Experiments with remote decision-making agents depend on more than the policy: the world may change during inference, the simulator determines which...
The paper investigates whether centralized learning improves coordination among embodied agents by comparing independent Q‑learning (IQL) and central Q‑learning (CQL) in a predator‑prey environment. Experiments on 5x5 and 8x8 grids with varying speed and stamina show that CQL does not enhance coordination and can even harm performance; its only advantage is a 42% reduction in capture time on the small grid when the best joint action is explored. The study concludes that tabular comparisons are reliable only when they assess state‑space coverage and implement methods consistently, and it proposes simple checks for these conditions.
arXiv:2607. 26034v1 Announce Type: new Abstract: Technological races create tension between speed and safety: actors may gain by moving faster than competitors, even when risky development is harmful.
arXiv:2605. 29874v2 Announce Type: replace-cross Abstract: Do next-generation LLM agents inherit the cooperative biases documented in their predecessors, or does scale and provider diversity reshape equilibrium behaviour in competitive multi-agent settings?