  We study a general class of repeated auctions, such as the ones found in
electricity markets, as multi-agent games between the bidders. In such a
repeated setting, bidders can adapt their strategies online based on the data
observed in the previous auction rounds. Moreover, if no-regret algorithms are
employed by the bidders to update their strategies, the game is known to
converge to a coarse-correlated equilibrium, which generalizes the notion of
Nash equilibrium to a probabilistic view of the auction state. Well-studied
no-regret algorithms depend on the feedback information available at every
round, and can be mainly distinguished as bandit (or payoff-based), and
full-information. However, the information structure found in auctions lies in
between these two models, since participants can often obtain partial
observations of their utilities under different strategies. To this end, we
modify existing bandit algorithms to exploit such additional information.
Specifically, we utilize the feedback information that bidders can obtain when
their bids are not accepted, and build a more accurate estimator of the utility
vector. This results in improved regret guarantees compared to standard bandit
algorithms. Moreover, we propose a heuristic method for auction settings where
the proposed algorithm is not directly applicable. Finally, we demonstrate our
findings on case studies based on realistic electricity market models.
