Uncertainty modified policy for multi-agent reinforcement learning

Uncertainty in the evolution of opponent behavior creates a non-stationary environment for the agent, reducing the reliability of value estimation and strategy selection while compromising security during the exploration process. Previous studies have developed various uncertainty quantification techniques and designed uncertainty-aware exploration methods for multi-agent reinforcement learning (MARL). However, existing methods have gaps in theoretical research and experimental verification of decoupling uncertainty between opponents and environment, which can decrease learning efficiency and lead to an unstable training process. Due to inaccurate opponent modeling, the agent is vulnerable to harm from opponents, which is undesirable in real-world tasks. To address these issues, this study proposes a novel uncertainty-guided safe exploration strategy for MARL that decouples the two types of uncertainty originating from the environment and opponents. Specifically, we introduce an uncertainty decoupling quantification technique based on a novel variance decomposition method for action-value functions. Furthermore, we present an uncertainty-aware policy optimization mechanism to facilitate safe exploration in MARL. Finally, we propose a new adaptive parameter scaling method to ensure efficient exploration by the agents. Theoretical analysis establishes the proposed approach’s convergence rate, and its effectiveness is demonstrated empirically. Extensive experiments on benchmark tasks spanning differential games, multi-agent particle environments, and RoboSumo validate the proposed uncertainty-guided method’s significant advantages in attaining higher scores and facilitating safe agent exploration.