Generalized multi-agent competitive reinforcement learning with differential augmentation

Observation generalization in multi-agent reinforcement learning is essential for real-world applications as enables agents to formulate effective policies to deal with environmental uncertainty. However, most existing studies seldom consider the generalization ability of agents in competitive scenarios, which is not in line with the actual process of intelligent groups in real applications. We propose a generalized multi-agent reinforcement learning framework (GMARL) by incorporating the data augmentation mechanism of differential privacy, which enhances the agent's adaptability and improves the model's stability. Specifically, we introduce a Laplacian mechanism with temporary adjacency sensitivity into the training and add an observation supplement to the agent's input. Under this mechanism, the agent intelligently considers the environment fluctuation, thus guarding against the adverse effects of the uncertain changes in the environment states. We show the rigorous guarantee of the difference balance between the observation supplement and the original data, and propose series of variants upon the GMARL framework. Extensive experimental results on five benchmark multi-agent competitive environments against with five state-of-the-art algorithms validate the effectiveness and robustness of our proposed method.