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Existing multi-agent PPO algorithms lack compatibility with different typesof parameter sharing when extending the theoretical guarantee of PPO tocooperative multi-agent reinforcement learning (MARL). In this paper, wepropose a novel and versatile multi-agent PPO algorithm for cooperative MARL toovercome this limitation. Our approach is achieved upon the proposedfull-pipeline paradigm, which establishes multiple parallel optimizationpipelines by employing various equivalent decompositions of the advantagefunction. This procedure successfully formulates the interconnections amongagents in a more general manner, i.e., the interconnections among pipelines,making it compatible with diverse types of parameter sharing. We provide asolid theoretical foundation for policy improvement and subsequently develop apractical algorithm called Full-Pipeline PPO (FP3O) by several approximations.Empirical evaluations on Multi-Agent MuJoCo and StarCraftII tasks demonstratethat FP3O outperforms other strong baselines and exhibits remarkableversatility across various parameter-sharing configurations.

FP3O: Enabling Proximal Policy Optimization in Multi-Agent Cooperation with Parameter-Sharing Versatility