Diverse Policy

Diverse policy research focuses on developing methods for reinforcement learning agents to learn multiple, distinct, high-performing policies for a given task, rather than a single optimal one. Current research emphasizes techniques like using transformer architectures, multi-objective optimization, and behavior distillation to generate and manage policy diversity, often incorporating measures to ensure both performance and behavioral distinctiveness. This area is significant because diverse policies enhance robustness, adaptability, and generalization capabilities in complex environments, with applications ranging from robotics and game playing to resource allocation and multi-agent systems.