Sellers´ Pricing By Bayesian Reinforcement Learning

Q-learning is a Reinforcement Learning (RL) model from the field of artificial intelligence, several papers studied the use of Q-learning for modeling the problem of dynamic pricing in electronic marketplaces. But existing RL comes short of achieving good result as the amount of exploration required is often too costly. To address the problem of dynamic pricing, we take a Bayesian model-based approach, framing transition function and reward function of MDP as distributions, and use sampling technique for action selection. The Beyesian approach accounts for the general problem of exploration-exploitation tradeoff. Simulations show the pricing algorithm improves the profits compares with other pricing strategies based on the same pricing model.