Learning competitive pricing strategies by multi-agent reinforcement learning
✍ Scribed by Erich Kutschinski; Thomas Uthmann; Daniel Polani
- Publisher
- Elsevier Science
- Year
- 2003
- Tongue
- English
- Weight
- 218 KB
- Volume
- 27
- Category
- Article
- ISSN
- 0165-1889
No coin nor oath required. For personal study only.
✦ Synopsis
In electronic marketplaces automated and dynamic pricing is becoming increasingly popular. Agents that perform this task can improve themselves by learning from past observations, possibly using reinforcement learning techniques. Co-learning of several adaptive agents against each other may lead to unforeseen results and increasingly dynamic behavior of the market. In this article we shed some light on price developments arising from a simple price adaptation strategy. Furthermore, we examine several adaptive pricing strategies and their learning behavior in a co-learning scenario with di erent levels of competition. Q-learning manages to learn best-reply strategies well, but is expensive to train.