The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
arXiv:2610.20486v1 Announce Type: new Abstract: In reinforcement learning, an agent must explore an unknown environment to learn how to make good decisions. Unlike methods that can freely sample any...
The paper investigates learning Nash equilibria in partially observable Markov games (POMGs) where agents cannot fully observe the state. By focusing on a subclass with independent state transitions and a Markov potential game structure, the authors propose an independent learning algorithm that allows agents to converge to an approximate Nash equilibrium using only their own observations and actions, without communication. Under a filter stability assumption, finite‑history policies are shown to approximate the POMG sufficiently, enabling a surrogate near‑potential Markov game and yielding quasi‑polynomial sample and computational complexity.
arXiv:2602. 16965v2 Announce Type: replace Abstract: We study the decentralized multi-player stochastic bandit problem over a continuous, Lipschitz-structured action space where hard collisions yield zero reward.
The paper introduces two algorithms for safe multi‑objective reinforcement learning that guarantee zero constraint violation during learning. The first algorithm learns a safe Pareto‑optimal policy for a given preference by combining optimistic reward estimates with pessimistic cost bounds, achieving an ×O(1/√T) suboptimality gap. The second, preference‑free algorithm safely explores first and then computes a safe policy for any preference, retaining the same safety guarantee and achieving an ×O(1/√T+1/√K) suboptimality gap.
arXiv:2603.18965v2 Announce Type: replace Abstract: Maximum entropy reinforcement learning motivates agents to explore states and actions to maximize the entropy of some distribution, typically by pr...
arXiv:2606. 11284v1 Announce Type: cross Abstract: Real-world multi-agent systems, from traffic coordination to resource allocation, are often modeled as general-sum games where individual incentives conflict with collective welfare.