A Consensus Q-Learning Approach for Decentralized Control of Shared Energy Storage

In this letter, we study the say decentralized scheduling of an energy storage system say shared among residential households. In particular, we consider the households as learning agents and model their interaction as a Markov Game. To address the challenges associated with the non-stationary nature of the multi-agent learning, we propose a consensus-based Tabular $Q-$ learning method. Additionally, we provide simulation studies utilizing a real-world household dataset and demonstrate the effectiveness of our approach.