Multi-Agent Local Information Reconstruction for Situational Cognition

Learning an effective strategy is challenging for agents in partially observable environment, where the agents can only observe a part of environment information and make decisions based on local information. Hence, how to effectively utilize local information to achieve efficient cooperation among the agents is particularly important. The agents can establish an understanding of themselves and their surrounding environment based on their historical observation information. However, the understanding lacking global information is local or limited, resulting in low performance in some complex tasks. To solve the problem, a situational cognition learning framework is proposed for each agent based on local information, with which each agent can reconstruct a cognition about itself and its surrounding environment and map it into a high dimensional representation space. In particular, situational cognition is modeled as a random variable under the condition of local trajectory. In addition, an information regularizer is introduced to ensure that the situational cognition is complete and accurate through maximizing the mutual information between the situational cognition and global information, conditioned on the local trajectory of the agent. Various simulations are conducted and show that the proposed framework significantly promotes cooperation among the agents and improves performance.