Reward Free

Reward-free reinforcement learning focuses on training agents to explore and learn useful skills without relying on explicit reward signals during the initial learning phase. Current research emphasizes efficient exploration strategies, often employing intrinsic motivation methods, diffusion models, or ensemble approaches to discover diverse behaviors and build robust world models. This research aims to improve sample efficiency and generalization capabilities, leading to agents that can rapidly adapt to new tasks with minimal labeled data, thereby advancing both theoretical understanding and practical applications of reinforcement learning. The ultimate goal is to create more generalizable and data-efficient AI agents.