Multi-agent reinforcement learning based on local communication

Aiming at the locality and uncertainty of observations in large-scale multi-agent application scenarios, the model of Decentralized Partially Observable Markov Decision Processes (DEC-POMDP) is considered, and a novel multi-agent reinforcement learning algorithm based on local communication is proposed. For a distributed learning environment, the elements of reinforcement learning are difficult to describe effectively in local observation situation, and the learning behaviour of each individual agent is influenced by its teammates. The local communication with consensus protocol is utilized to agree on the global observing environment, and thus that a part of strategies generated by repeating observations are eliminated, and the agent team gradually reach uniform opinion on the state of the event or object to be observed, they can thus approach a unique belief space regardless of whether each individual agent can perform a complete or partial observation. The simulation results show that the learning strategy space is reduced, and the learning speed is improved.