  This paper proposes a reinforcement learning based dynamic matching framework
for power markets. These markets are challenging because of their dynamic
nature and the uncertainty over the future load and generation. We propose a
novel hybrid model for the matching policy that is a composition of a fixed
rule-based function and a trainable component that can be trained from market
data with no prior knowledge or expert supervision. The output of the trainable
component is a probability distribution over the matching decisions for the
individual customers. The hybrid model we propose enables the learning
algorithm to find an effective matching policy and simultaneously satisfy the
load servicing constraints. We show through extensive simulations that the
learning algorithm learns an effective matching policy for different
generation-consumption profiles and exhibits better performance compared to
standard online matching heuristics such as Match on Arrival (MA), Match to the
Highest (MH) and Match to the Earliest Deadline First (MED).
