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

@inproceedings{Sridharan2000MultiagentQA,
  title={Multi-agent Q-learning and Regression Trees for Automated Pricing Decisions},
  author={Manu Sridharan and Gerald Tesauro},
  booktitle={ICML},
  year={2000}
}
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… CONTINUE READING
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