Publication

Multi-Agent Reinforcement Learning in Partially Observable Environments Using Social Learning

Ali H. Sayed, Ainur Zhaikhan
2025

Abstract

This work employs a social learning strategy to estimate the global state in a partially observable multi-agent reinforcement learning (MARL) setting. We prove that the proposed methodology can achieve results within an ε-neighborhood of the solution for a fully observable setting, provided that a sufficient number of social learning updates are performed. We illustrate the results through computer simulations.

Official source

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.