Advantage Constrained Proximal Policy Optimization in Multi-Agent Reinforcement Learning

We investigate the integration of value-based and policy gradient methods in multi-agent reinforcement learning (MARL). The Individual-Global-Max (IGM) principle plays an important role in value-based MARL, as it ensures consistency between joint and local action values. IGM is difficult to guarantee in multi-agent policy gradient methods due to stochastic exploration and conflicting gradient directions. In this paper, we propose a novel multi-agent policy gradient algorithm called Advantage Constrained Proximal Policy Optimization (ACPPO). ACPPO calculates each agent's current local state-action advantage based on their advantage network and estimates the joint state-action advantage based on multi-agent advantage decomposition lemma. According to the consistency of the estimated joint-action advantage and local advantage, the coefficient of each agent constrains the joint-action advantage. ACPPO, unlike previous policy gradient MARL algorithms, does not require an additional sampled baseline to reduce variance or a sequential scheme to improve accuracy. The proposed method is evaluated using the continuous matrix game, the Starcraft Multi-Agent Challenge, and the Multi-Agent MuJoCo task. ACPPO outperforms baselines such as MAPPO, MADDPG, and HATRPO, according to the results.