What are the benefits and challenges of using centralized vs decentralized learning in MADQL?
Multi-agent deep Q-learning (MADQL) is a reinforcement learning technique that allows multiple agents to learn from their own experiences and interactions in a shared environment. MADQL can be applied to various domains, such as robotics, games, and social dilemmas, where coordination, cooperation, or competition among agents are essential. However, MADQL also faces some challenges, such as scalability, stability, and communication, that depend on how the learning process is organized. In this article, you will learn about the benefits and challenges of using centralized vs decentralized learning in MADQL, and some examples of how they are implemented in different scenarios.