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arXiv Machine Learning

Spatial Pattern Formation from Multi-Agent Learning in Public Goods Dilemmas

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.

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arXiv AI
Oct 5

LLM-Guided Reinforcement Learning with Representative Agents for Traffic Modeling

The paper proposes a scalable traffic modeling approach that uses a single representative large language model (LLM) agent for each homogeneous traveler group, rather than one LLM per traveler. The representative agent maintains a mixed strategy over routes, updates it daily based on positive reinforcement signals, and uses a tunable step size to adjust its strategy. This design improves scalability, stabilizes learning, and produces interpretable dynamics that reproduce realistic behavioral patterns such as the decoy effect and income‑based willingness‑to‑pay differences.

By Hanlin Sun, Jiayang Li
llmsagentsreinforcement-learningmultimodal
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arXiv Machine Learning
3d ago

Self-organizing reinforcement learning: a learner-free swarm approaches the explore-exploit optimum

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.

By Galen J. Wilkerson
agentsreinforcement-learningrobotics
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arXiv AI
Sep 24

Evolutionary Stability Does Not Guarantee Learning Accessibility: A Multi-Agent Reinforcement Learning Perspective on Cooperation Emergence

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.

By Yijie Wang
agentsreinforcement-learning
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arXiv AI
Sep 18

Mitigating Retaliatory Algorithmic Collusion in Repeated Games

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.

By Karthik Sivachandran, Rohan Paleja
agentsreinforcement-learning
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arXiv AI
Oct 7

Self-Referenced Social Preferences: Cooperation without Observing Others Rewards

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.

By Mohamed Ayman Mohamed, Harshil Kotamreddy, Marcos Menon Jose
agentsreinforcement-learning
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arXiv Machine Learning
2d ago

Learning to Incentivise: Using Reinforcement Learning for Sustainable Urban Mobility

The paper proposes a multi‑agent reinforcement learning framework that dynamically allocates incentives to drivers, encouraging them to take alternative routes. By doing so, it reduces total travel time by up to 16% and CO₂ emissions by 9% under an unlimited budget, while revealing a trade‑off between the two objectives. The study demonstrates that a carefully weighted multi‑objective function can improve both travel time and emissions beyond baseline levels.

By Germ\'an Pardo-Gonz\'alez, Shaghayegh Vosough, Katerina Papadaki, Claudio Roncoli
agentsreinforcement-learning
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