Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning. (arXiv:2205.06760v1 [cs.AI])

Advances in artificial intelligence often stem from the development of new
environments that abstract real-world situations into a form where research can
be done conveniently. This paper contributes such an environment based on ideas
inspired by elementary Microeconomics. Agents learn to produce resources in a
spatially complex world, trade them with one another, and consume those that
they prefer. We show that the emergent production, consumption, and pricing
behaviors respond to environmental conditions in the directions predicted by
supply and …

ai arxiv learning reinforcement reinforcement learning