Model Learning and Knowledge Sharing for Cooperative Multiagent Systems in Stochastic Environment.

ABSTRACT: An imposing task for a reinforcement learning agent in an uncertain environment is to expeditiously learn a policy or a sequence of actions, with which it can achieve the desired goal. In this article, we present an incremental model learning scheme to reconstruct the model of a stochastic environment. In the proposed learning scheme, we introduce a clustering algorithm to assimilate the model information and estimate the probability for each state transition. In addition, utilizing the reconstructed model, we present an experience replay strategy to create virtual interactive experiences by incorporating a balance between exploration and exploitation, which greatly accelerates learning and enables planning. Furthermore, we extend the proposed learning scheme for a multiagent framework to

SUBMITTER: Jiang WC 

PROVIDER: S-EPMC7338261 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature