arXiv AI
A learner-free swarm reaches the explore-exploit equilibrium: A self-organized attractor approximating the reinforcement-learning solution
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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.
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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.
arXiv AI
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Effectiveness of Curriculum Learning Depends on Reward Sparsity and Competing Optima
The paper investigates how curriculum learning affects reinforcement learning in a minimal policy learning model. It shows that when rewards are dense, curricula offer only marginal benefits, but when rewards are sparse, curricula become essential for learning to occur. The study also highlights that competing objectives—minimizing effort versus maximizing reward—create distinct optima that curricula can help navigate by reducing reward sparsity and reshaping the reward landscape.
arXiv AI
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Reinforcing the World's Edge: A Continual Learning Problem in the Multi-Agent-World Boundary
The paper studies a stationary decentralized Markov game where a focal agent experiences drifting rewards and dynamics due to learning peers, framing this as an agent‑centric continual reinforcement‑learning problem. It introduces the concept of an invariant core—maximal abstract patterns common to many successful trajectories—and proves a worst‑case conditioning theorem linking trajectory‑law drift to success coverage. The authors provide theoretical guarantees for survival horizon, first‑exit law, and regret, and validate their predictions with solvable models and empirical studies in continual control, cue‑MNIST, and Level‑Based Foraging.
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Evolutionary Stability Does Not Guarantee Learning Accessibility: A Multi-Agent Reinforcement Learning Perspective on Cooperation Emergence
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...