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Distributed energy resources (DERs), such as rooftop solar panels, aregrowing rapidly and are reshaping power systems. To promote DERs,feed-in-tariff (FIT) is usually adopted by utilities to pay DER owners certainfixed rates for supplying energy to the grid. An alternative to FIT is a marketbased approach; i.e., consumers and DER owners trade energy in an auction-basedpeer-to-peer (P2P) market, and the rates are determined by a market clearingprocess. However, the complexities in sucha market and agents' boundedrationality may invalidate many well-established theories on auction design andhinder market development. To address this issue, we propose an automatedbidding framework in a repeated auction based on multi-armed bandit learning,which aims to minimize each bidder's cumulative regret. Numerical resultsindicate convergence of such a multi-agent learning game to a steady-state. Forcomparison purpose, we apply the framework to three different auction designsto realize a P2P market.

Multi-Agent Learning in Double-side Auctions forPeer-to-peer Energy Trading