VS-Bench is a new benchmark of ten visual multi-agent environments that measures VLMs on element recognition, next-action prediction, and normalized episode return, showing strong perception but large gaps in reasoning and decision-making with the best model at 46.6% prediction accuracy and 31.4% of
Stochastic games
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A model-free RL methodology is developed to maximize the probability of LTL satisfaction in unknown stochastic games when the derived DRA has a single Rabin pair, with a generalization providing lower bounds for multiple pairs.
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VS-Bench: Evaluating VLMs for Strategic Abilities in Multi-Agent Environments
VS-Bench is a new benchmark of ten visual multi-agent environments that measures VLMs on element recognition, next-action prediction, and normalized episode return, showing strong perception but large gaps in reasoning and decision-making with the best model at 46.6% prediction accuracy and 31.4% of
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Model-Free Reinforcement Learning for Stochastic Games with Linear Temporal Logic Objectives
A model-free RL methodology is developed to maximize the probability of LTL satisfaction in unknown stochastic games when the derived DRA has a single Rabin pair, with a generalization providing lower bounds for multiple pairs.