Decentralized Reinforcement Learning

Decentralized reinforcement learning (DRL) focuses on enabling multiple agents to learn optimal policies collaboratively without a central controller, aiming for efficient and robust solutions in complex, multi-agent environments. Current research emphasizes developing algorithms that balance exploration and exploitation effectively, often employing actor-critic architectures with centralized training for decentralized execution or novel approaches leveraging consensus-based updates and momentum-based variance reduction. This field is significant because it addresses scalability and robustness challenges inherent in centralized approaches, with applications ranging from multi-robot systems and traffic control to resource management and wireless networking.