Multi-agent Q-learning and Regression Trees for Automated Pricing Decisions

We study the use of single-agent and multi-agent Q-learning to learn seller pricing strategies in three diierent two-seller models of agent economies, using a simple regression tree approximation scheme to represent the Q-functions. Our results are highly encouraging { regression trees match the training times and policy performance of lookup table Q-learning, while ooering signiicant advantages in storage size and amount of training data required, and better expected scaling to large numbers of agents. Clear advantages are seen over neural networks, which yield inferior policies and require much longer training times. Our work is among the rst to demonstrate success in combining Q-learning with regression trees. Also, with regression trees, Q-learning appears much more feasible as a practical approach to learning strategies in large multi-agent economies.