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.
The paper demonstrates that a minimal foraging swarm can approach the explore/exploit optimum of reinforcement learning without any learner or reward signal. Agents use heavy‑tailed Levy flights to explore, recruit neighbors via a threshold cascade on their proximity network, and disperse as patch crowds, creating a cycle that balances exploration and exploitation. The swarm attains the shape of the ideal‑free distribution (committed foragers track richness with slope 0.94) and reaches this allocation dynamically in about 150 steps, though it remains slightly exploitable and does not achieve the exact equilibrium.
arXiv:2608. 20016v1 Announce Type: cross Abstract: Reputation is widely recognized as a key mechanism for sustaining cooperation.
The study investigates how spatial patterns arise when agents in public goods dilemmas learn movement strategies via tabular Q‑learning. Cooperators tend to cluster around resource peaks, while co‑adaptation with defectors can alter cluster strength and motion, sometimes producing traveling bands. Higher cooperator learning rates combined with lower defector rates lead to the greatest welfare losses, and crowding penalties can mitigate these losses, linking learning rates to spatial organization and collective welfare.
arXiv:2604.21155v2 Announce Type: replace Abstract: Intrinsic motivations are receiving increasing attention, i.e. behavioral incentives that are not engineered, but emerge from the interaction of an...
The paper proposes a new framework for collective information engines that rely on role differentiation rather than consensus. By modeling anti‑coordination games, agents infer roles from noisy social signals tied to persistent identities, and role‑following actions reinforce those identities, creating a feedback loop that can drive collective order. The authors show that when a social loop gain—determined by identity persistence, cognitive capacity, channel fidelity, and schema strength—exceeds one, roles emerge in a bifurcation cascade whose type is selected by resource‑driven replicator dynamics, offering a mechanistic basis for distributional AGI takeoff and a control lever for platform design.
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:2609.17325v1 Announce Type: new Abstract: Biological cells can be viewed as individual, interacting agents whose collective dynamics give rise to adaptive behaviour at multiple levels of organi...
The paper introduces self‑referenced social preferences, allowing agents to learn cooperative behavior without observing others’ private rewards. Each agent models its own reward, applies this model to observed transitions of other agents, and uses the resulting assessments to inform either the learning reward or policy updates. Experiments on three social‑dilemma environments show that agents can achieve cooperation and more equitable outcomes even when independent learners fail, with the best integration point depending on the type of social preference used.
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.