Dynamic Pricing Agents and Multiagent Learning

Abstract. We implemented three different types of pricing agents in a simulated economy. Each type of agent is based on a different learning method. The first method is simple reinforcement learning. The second method is the traditional Q-learning method. The third method is Nash Q-learning method. In each simulation, there are two agents, and a fixed amount of customers. The agent that charges a lower price will attract all the customers. When both firms charge the same price, they receive equal share of the market. Our simulation shows that the two Q-learning methods that take future rewards into account perform better than the simple method that is myopic. Among the two Q-learning methods, Nash Q-learning performs consistently better than the Q-learning method. This suggests the importance of game theoretical modeling in online learning.