Efficient Exploration

Efficient exploration is one of the main obstacles to the large-scale application of modern deep reinforcement learning algorithms. The core challenge lies in balancing the exploitation of current estimates with the acquisition of information about unknown states and actions. Effectively addressing this issue can significantly enhance the performance and adaptability of reinforcement learning systems, improving their learning efficiency and decision quality in complex environments.