Experience Sharing

Experience sharing (ES) in multi-agent systems focuses on accelerating learning and improving performance by allowing agents to learn from each other's experiences. Current research emphasizes developing robust ES frameworks that address security and privacy concerns, particularly in decentralized environments, often employing techniques like weighted experience aggregation and differential privacy. This research is significant for advancing distributed reinforcement learning, enabling more efficient and secure collaboration in complex tasks across diverse applications, such as robotics and multi-task learning. Furthermore, investigations into the impact of social network structures on ES highlight the importance of communication topology in optimizing collective learning and innovation.