arXiv:2608. 20016v1 Announce Type: cross Abstract: Reputation is widely recognized as a key mechanism for sustaining cooperation.
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arXiv:2604. 15267v2 Announce Type: replace-cross Abstract: It is increasingly important that LLM agents interact effectively and safely with other goal-pursuing agents, yet, recent works report the opposite trend: LLMs with stronger reasoning capabilities behave _less_ cooperatively in mixed-motive games such as the prisoner's dilemma and public goods settings.
The paper introduces CURB, a reward‑shaping framework that penalizes the total variation distance between an agent’s action distributions under cooperation and defection histories, thereby preventing collusive equilibria in repeated games. By linking empirical Q‑learning collusion to Simple Penal Codes, the authors prove that any non‑trivial SPC can be neutralized, and demonstrate CURB’s effectiveness in both tabular and deep Q‑learning settings for Bertrand and Cournot competition.
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. 04710v1 Announce Type: new Abstract: Inducing cooperation among distributed agents is still a difficult problem in the field of multi-agent reinforcement learning (MARL), particularly in social dilemma situations.
Inducing cooperation among distributed agents is still a difficult problem in the field of multi-agent reinforcement learning (MARL), particularly in social dilemma situations. There, individual interests are misaligned with the common good and individual rationality leads to suboptimal group outcomes.
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.