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.
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The paper proposes a principled communication strategy for multi‑agent reinforcement learning that gates messages based on the KL divergence between agents’ belief distributions over a latent world state. Each agent maintains a softmax belief derived from its LSTM hidden state and only communicates when disagreement exceeds a fixed threshold. Experiments on Predator‑Prey and MPE simple_spread show that this KL‑belief gating can match or surpass existing methods, improving performance and reducing variance in certain settings.
arXiv:2609.34373v2 Announce Type: replace Abstract: Rate-limited multi-agent teams raise three questions the emergent-communication literature has answered only empirically: what an optimal message s...
arXiv:2601. 17454v2 Announce Type: replace-cross Abstract: Centralized value learning underlies a broad class of multi-agent reinforcement learning methods, but its claimed advantage is typically evaluated in settings that confound coordination structure with function approximation and partial observability.
arXiv:2609.39342v1 Announce Type: cross Abstract: Intrinsic motivation plays a central role in adaptive and goal-directed behavior by conferring agents reward-independent objectives and biases useful...
Intrinsic motivation plays a central role in adaptive and goal-directed behavior by conferring agents reward-independent objectives and biases useful to act in noisy and uncertain environments. Active...
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.