Policy Perturbation via Noisy Advantage Values for Cooperative Multi-agent Actor-Critic methods

Multi-Agent Reinforcement Learning (MARL) has seen revolutionary breakthroughs with its successful application to multi-agent cooperative tasks such as robot swarms control, autonomous vehicle coordination, and computer games. Recent works have applied the Proximal Policy Optimization (PPO) to the multi-agent tasks, such as Independent PPO (IPPO); and vanilla Multi-agent PPO (MAPPO) which has a centralized value function. However, previous literature shows that MAPPO may not perform as well as Independent PPO (IPPO) and the Fine-tuned QMIX. Thus MAPPO-Feature-Pruned (MAPPO-FP) further improves the performance of MAPPO by the carefully designed artificial features. In addition, there is no literature that gives a theoretical analysis of the working mechanism of MAPPO. In this paper, we firstly theoretically generalize single-agent PPO to the vanilla MAPPO, which shows that the vanilla MAPPO is approximately equivalent to optimizing a multi-agent joint policy with the original PPO. Secondly, we find that MAPPO faces the problem of \textit{The Policies Overfitting in Multi-agent Cooperation(POMAC)}, as they learn policies by the sampled centralized advantage values. Then POMAC may lead to updating the policies of some agents in a suboptimal direction and prevent the agents from exploring better trajectories. To solve the POMAC, we propose two novel policy perturbation methods, i.e, Noisy-Value MAPPO (NV-MAPPO) and Noisy-Advantage MAPPO (NA-MAPPO), which disturb the advantage values via random Gaussian noise. The experimental results show that the performance of our methods is better than that of Fine-tuned QMIX and MAPPO-FP, and achieves SOTA in Starcraft Multi-Agent Challenge (SMAC). We open-source the code at \url{https://github.com/hijkzzz/noisy-mappo}.