Reinforcement learning (RL) has emerged as a powerful paradigm for enabling intelligent agents to solve sequential decision-making problems under uncertainties. It has witnessed remarkable successes in various domains, ranging from game-playing agents to autonomous systems. However, as real-world challenges become increasingly intricate and interconnected, there is a need to go beyond the single-agent framework. Multi-agent reinforcement learning (MARL), is an extension of RL that enables multiple agents to learn and interact, introducing a new dimension of complexity and sophistication.

This talk delves into the exciting realm of RL and MARL, exploring the foundational principles, recent advancements, and promising applications of these techniques. We begin by introducing the core concepts of RL. Building upon this foundation, we shift our focus to MARL, where multiple agents learn simultaneously, either cooperating or competing with each other. Then, we examine the challenges posed by MARL, including coordination, communication, and the exploration-exploitation dilemma.