A New Technique to Accelerate the Learning Process in Agents based on Reinforcement Learning
Noureddine El Abid Amrani, EZZRHARI Fatima Ezzahra, Mohamed Youssfi, Sidi Mohamed Snineh, Omar Bouattane · Advances in Science Technology and Engineering Systems Journal · 2022
The use of decentralized reinforcement learning (RL) in the context of multi-agent systems (MAS) poses some difficult problems.The speed of the learning process for example.Indeed, if the convergence of these algorithms has been widely studied and mathematically proven, they suffer from being very slow.In this context, we propose to use RL in MAS in an intelligent way to speed up the learning process in these systems.The idea is to consider the MAS as a new environment to be explored and the communication, between the agents, is limited to the exchange of knowledge about the environment.The last agent to explore the environment has to communicate the new knowledge to the other agents, and the latter have to build their knowledge bases taking into account this knowledge.To validate our method, we chose to evaluate it in a grid environment.Agents must exchange their tables (Qtables) to facilitate better exploration.The simulation results show that the proposed method accelerates the learning process.Moreover, it allows each agent to reach its goal independently.