Structure Learning in Human Sequential Decision-Making

• Authors: Daniel Acuña, Paul Schrater
• Year: 2009
• Abstract: We use graphical models and structure learning to explore how people learn policies in sequential decision making tasks. Studies of sequential decision-making in humans frequently find suboptimal performance relative to an ideal actor that knows the graph model that generates reward in the environment. We argue that the learning problem humans face also involves learning the graph structure for reward generation in the environment. We formulate the structure learning problem using mixtures of reward models, and solve the optimal action selection problem using Bayesian Reinforcement Learning. We show that structure learning in one and two armed bandit problems produces many of the qualitative behaviors deemed suboptimal in previous studies. Our argument is supported by the results of experiments that demonstrate humans rapidly learn and exploit new reward structure.
• Conference proceedings: In Yoshua Bengio & Dale Schuurmans (Eds.), Proceedings of the 2008 Neural Information Processing Systems, Vancouver, Canada.
• Files: Paper [PDF]

The views and opinions expressed in this page are strictly those of the page author.
The contents of this page have not been reviewed or approved by the University of Minnesota.