<ul><li>Researchers study a long-run mean-variance team stochastic game (MV-TSG) and propose a Mean-Variance Multi-Agent Policy Iteration (MV-MAPI) algorithm.</li><li>The MV-TSG faces challenges with the non-additive and non-Markovian variance metric, as well as non-stationary environment due to simultaneous policy updates.</li><li>The MV-MAPI algorithm converges to a first-order stationary point, with specific conditions for local Nash equilibria and local optima.</li><li>To solve large-scale MV-TSGs with unknown environmental parameters, a multi-agent reinforcement learning algorithm named MV-MATRPO is proposed.</li></ul>

Policy Optimization and Multi-agent Reinforcement Learning for Mean-variance Team Stochastic Games

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