The paper presents a symmetric solution to the Bellman optimality equation for a repeated harmony game, revealing three distinct symmetric solutions. One solution corresponds to the trivial All‑Cooperate strategy, another aligns with the Win‑stay Lose‑shift strategy known from the prisoner's dilemma, and a third nontrivial strategy is explored in detail. The authors also numerically examine which of these strategies agents learn using a reinforcement learning algorithm.
arXiv:2608.28977v1 Announce Type: new Abstract: Several works have investigated the influence of graph topology on cooperation among artificial agents, while the majority of the literature has focuse...
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.
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.
The paper investigates whether evolutionary stability guarantees that learning agents can achieve cooperative outcomes in a multi‑agent setting. Using a three‑agent governance game, the authors compare the evolutionary basin of attraction with learning basins derived from independent Q‑learning, scaled Boltzmann exploration, and SA–EA BQL. They find that while the evolutionary basin covers the entire sampled grid, only ε‑greedy Q‑learning attains a substantial learning basin, whereas the other methods fail to sustain cooperation, highlighting a disconnect between population‑level stability and finite‑sample learning accessibility.
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.
arXiv:2411. 11793v2 Announce Type: replace Abstract: In federated learning (FL), a central server typically allocates training efforts to clients.
arXiv:2510. 14907v2 Announce Type: replace-cross Abstract: We extend the study of learning in games to dynamics that exhibit non-asymptotic stability.
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.
Cooperation emergence is a central problem in multi-agent systems because decentralized agents must coordinate while adapting to the changing behavior of others. Evolutionary game theory identifies st...
The paper introduces Preference-based Opponent Shaping (PBOS), a method that incorporates a preference parameter into an agent’s loss function to directly consider an opponent’s loss during strategy updates. By jointly learning strategy and preference parameters, PBOS aims to guide agents toward cooperative or competitive behaviors without relying on simple opponent predictions. Experiments on differentiable games demonstrate that PBOS enables agents to achieve better reward distributions across various environments.
The paper investigates collusive behavior in a two‑player Almgren‑Chriss liquidation game using independent proximal policy optimisation agents. By training agents that learn a punitive mechanism, the study shows that when one agent deviates from the mean liquidation schedule, the other responds by accelerating liquidation, thereby negating the deviator’s gains while keeping its own payoff stable. The authors formalize checks confirming that punishment outweighs deviation gains and that behavioral changes are sufficient to offset losses, providing both behavioral and economic evidence of collusion.
arXiv:2506. 03802v2 Announce Type: replace Abstract: We introduce a learning problem in a generalized two-sided matching market, where agents select actions to interact with their match.