Exploring Agent Behavior and Performance in Shortened Simulations via Multiagent Reinforcement Learning

Abstract

This study explores the potential of Multiagent Reinforcement Learning (MARL) for autonomous navigation in a discrete two-dimensional environment. We design and implement an agent that learns an optimal path through a 5×5 grid via repeated interactions and a reward-based mechanism. Over 500 training episodes, we examine the convergence speed of the learned policy, the stability of agent behavior, and the success rate in reaching the goal. Our approach combines artificial neural networks with a multiagent framework, enabling decentralized decision making and scalable adaptation. We discuss critical factors affecting learning stability, including reward function design and network architecture, and outline avenues for extending the methodology to more complex, real-time tasks. The findings demonstrate MARL’s promise in solving navigation problems efficiently and provide concrete recommendations for tuning training parameters and network structures to enhance performance and robustness.