Reinforcement Learning for Scientific Application: A Survey

Reinforcement learning is an algorithm that learns optimal policies through trial and error. In application domains, reinforcement learning has been successfully applied to many fields such as AlphaGo and autonomous driving systems. As the potential of reinforcement learning is gradually discovered, its application fields are expanding, and it is gradually applied to many fields such as energy management, scientific discovery, robot control, autonomous driving, and more. However, as the environment becomes increasingly complex, a single agent often struggles when facing complex and unstable environments. Thus, multi-agent reinforcement learning has emerged. This method extends reinforcement learning to collaborative or competitive tasks among multiple agents and is an effective means of solving complex problems. In order to better utilize reinforcement learning methods to explore scientific problems, efficient large-scale training techniques are often needed. The development of efficient training techniques and scientific applications permeate and integrate with each other, providing a solid foundation for reinforcement learning in exploring scientific problems. It is foreseeable that the combination of reinforcement learning and scientific applications will continue to be an important direction for future artificial intelligence research, and will have increasingly extensive and profound impacts.