Bayesian neural networks in the multiagent environment
Authors: Podmaryov M.S.  
Published in issue: #3(92)/2024  
DOI:  
Category: Informatics, Computer Engineering and Control  Chapter: Information Technology. Computer techologies. Theory of computers and systems 

Keywords: multiagent environment, machine learning, multiagent reward learning, neural networks, Bayesian neural networks, Bayesian ActorCritic algorithm 

Published: 25.07.2024 
The paper considers behavioral features of the Bayesian neural networks in the multiagent environment. It presents reward learning, multiagent reward learning, and the Bayesian ActorCritic behavior in the multiagent environment. Learning using the Bayesian neural networks is analyzed. The Bayesian neural network architecture and hierarchy are demonstrated. The paper describes agent components and their interaction with the testing environment. The Mujoco environment is selected in testing. The environment features and aspects are highlighted. Tests were conducted to check behavior of the agents controlled by the Bayesian neural network using the ActorCritic algorithm. Test results are presented, they prove the algorithm efficiency, achieving successful learning, and interaction between the agents and the environment. The work performed is acknowledging that the Bayesian neural networks and the Bayesian ActorCritic algorithm are capable of learning and achieving set results in the multiagent environment.
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