Hierarchical Reinforcement Learning as a Model of Human Task Interleaving

Understand

How do people decide how long to continue in a task, when to switch, and to which other task? Understanding the mechanisms that underpin task interleaving is a long-standing goal in the cognitive sciences.

• Prior work suggests greedy heuristics and a policy maximizing the marginal rate of return.
• However, it is unclear how such a strategy would allow for adaptation to everyday environments that offer multiple tasks with complex switch costs and delayed rewards.
• Here we develop a hierarchical model of supervisory control driven by reinforcement learning (RL).