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New method synthesizes policies for finite MDP agent populations

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for policy synthesis in multi-agent systems, specifically addressing finite populations of Markov decision process (MDP) agents. This approach tackles aggregate reach-avoid chance constraints, which are crucial for scenarios where standard mean-field methods fall short due to stochastic fluctuations in smaller agent fleets. The technique propagates the variance of the empirical density alongside the mean-field trajectory, using a discrete-time Lyapunov recursion and the Cantelli inequality to transform chance constraints into deterministic conditions on moments. This allows for gradient-based policy synthesis and provides a finite-N certificate for rigorous validation, demonstrated on gridworld and EV-charging aggregation problems. AI

IMPACT Introduces a novel method for policy synthesis in finite multi-agent systems, improving control under stochastic conditions.

RANK_REASON This is a research paper detailing a novel method for policy synthesis in multi-agent systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method synthesizes policies for finite MDP agent populations

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COVERAGE [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Anamika Dubey ·

    Policy Synthesis for Finite Populations of MDP Agents under Aggregate Reach-Avoid Chance Constraints

    Consider a finite population of agents with decoupled Markov transition dynamics and empirical-density feedback, subject to the following constraints: with probability at least $1-δ_r$, at least a fraction $α_r$ of agents must reach a target region at some time $t^*$, while, at e…

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