Decentralized Execution

Decentralized execution in multi-agent systems aims to achieve coordinated behavior among agents without relying on a central controller, improving scalability and robustness. Current research focuses on developing algorithms and architectures, such as multi-agent reinforcement learning with centralized training for decentralized execution (CTDE) and decentralized federated learning, that balance the benefits of centralized training with the need for independent agent action during deployment. This approach holds significant promise for applications ranging from traffic management and robotics to distributed machine learning, offering solutions to complex problems that are intractable for purely centralized or decentralized methods.