Yasai Wang

Abstract Brain-inspired reinforcement learning is pivotal for artificial general intelligence, yet current artificial neural network-based hardware lacks critical biological mechanisms like third-terminal modulated eligibility traces and dynamic reward signaling. Emerging materials address these challenges by efficiently mimicking complex reinforcement learning dynamics.

1. Introduction As a new type of distributed energy system, the DC microgrid shows significant advantages in renewable energy access, energy management, and optimal utilization [1]. The DC microgrid is composed of a variety of distributed power sources, energy storage devices, loads, and the corresponding control and protection equipment. Its core goal is to ensure the safe and stable operation of the system while [2] realizing the efficient use of energy and economic operation.