  In this paper, a novel Q-learning based approach is proposed for estimating
the parameters of synchronous generators using PMU measurements. Event playback
is used to generate model outputs under different parameters for training the
agent in Q-learning. We assume that the exact values of some parameters in the
model are not known by the agent in Q-learning. Then, an optimal
history-dependent policy for the exploration-exploitation trade-off is planned.
With given prior knowledge, the parameter vector can be viewed as states with a
specific reward, which is a function of the fitting error compared with the
measurements. The agent takes an action (either increasing or decreasing the
parameter) and the estimated parameter will move to a new state. Based on the
reward function, the optimal action policy will move the parameter set to a
state with the highest reward. If multiple events are available, they will be
used sequentially so that the updated $\mathbfcal{Q}$-value can be utilized to
improve the computational efficiency. The effectiveness of the proposed
approach is validated by estimating the parameters of the dynamic model of a
synchronous generator.
