Existing multi-agent reinforcement learning methods are limited typically to a small number
of agents. When the agent number increases largely, the learning becomes intractable due
to the curse of the dimensionality and the exponential growth of agent interactions. In this
paper, we present Mean Field Reinforcement Learning where the interactions within the
population of agents are approximated by those between a single agent and the average
effect from the overall population or neighboring agents; the interplay between the two …

With increasing numbers of prosumers employed with multi-energy systems (MES) towards
higher energy utilization efficiency, an advanced energy management scheme is becoming
increasingly important. The incorporation of MES into the existential energy market holds
promise for future power systems. The continuous double auction (CDA) market, in a
decentralized manner, makes it ideal for enabling peer-to-peer (P2P) energy trading due to
its high transparency and efficiency. However, the CDA market is difficult to model when …