The rapidly changing architecture and functionality of electrical networks
and the increasing penetration of renewable and distributed energy resources
have resulted in various technological and managerial challenges. These have
rendered traditional centralized energy-market paradigms insufficient due to
their inability to support the dynamic and evolving nature of the network. This
survey explores how multi-agent reinforcement learning (MARL) can support the
decentralization and decarbonization of energy networks and mitigate the 12
associated challenges. This is achieved by specifying key computational
challenges in managing energy networks, reviewing recent research progress on
addressing them, and highlighting open challenges that may be addressed using
MARL.

电网架构和功能的快速变化以及可再生能源和分布式能源资源的不断增长导致了各种技术和管理挑战。本调研报告探讨了多智能体强化学习（MARL）如何支持能源网络的去中心化和脱碳，并缓解相关的挑战。通过指定管理能源网络中的关键计算挑战，回顾最新的研究进展，以及突显可以使用 MARL 解决的开放性挑战。

能源网络的多智能体强化学习：计算挑战、进展与开放问题

Multi-Agent Reinforcement Learning for Energy Networks: Computational  Challenges, Progress and Open Problems

Resistor networks have recently had a surge of interest as substrates for
energy-efficient self-learning machines. This work studies the computational
capabilities of these resistor networks. We show that electrical networks
composed of voltage sources, linear resistors, diodes and voltage-controlled
voltage sources (VCVS) can implement any continuous functions. To prove it, we
assume that the circuit elements are ideal and that the conductances of
variable resistors and the amplification factors of the VCVS's can take
arbitrary values -- arbitrarily small or arbitrarily large. The constructive
nature of our proof could also inform the design of such self-learning
electrical networks.

该论文研究了电阻网络作为能效自学习机器的基底的计算能力，并证明电压源、线性电阻、二极管和电压控制电压源（VCVS）组成的电阻网络能够实现任意连续函数，为设计自学习电阻网络提供了建设性的证明方法。