The invention provides a practical reinforcement learning automaton method for quotation optimization of a power generator under limited information. The method comprises the following steps: S1, initializing an action space probability density function and a historical income cache region of the power generation capacity of the power generator; S2, discretizing the probability density function into a discrete probability density function to obtain a plurality of sub-intervals, selecting an action corresponding to the sub-interval where the random number is located according to the cumulativeprobability of the sub-intervals, and submitting the selected action; S3, evaluating the environmental feedback, calculating clearing income, executing enhanced signal evaluation according to the clearing income, and storing the clearing income into the historical income cache region; S4, updating the discrete probability density function as linear operation of discrete values of the discrete probability density function and the discrete Gaussian neighborhood function at the end points of the subintervals; and S5, judging whether an iteration stopping standard is reached or not, if not, returning to the step S2, and if so, ending the optimization process.
Practical reinforcement learning automaton method for quotation optimization of power generator under limited information