Tomov, MomchilSchulz, EricGershman, Samuel2023-02-092021-01-28Tomov, Momchil S, Eric Schulz, and Samuel J Gershman. 2021. “Multi-Task Reinforcement Learning in Humans.” Nature Human Behaviour 5 (6): 764–73.https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37374299The ability to transfer knowledge across tasks and generalize to novel ones is an important hallmark of human intelligence. Yet not much is known about human multi-task reinforcement learning. We study participants’ behavior in a novel two-step decision making task with multiple features and changing reward functions. We compare their behavior to two state-of-the-art algorithms for multi-task reinforcement learning, one that maps previous policies and encountered features to new reward functions and one that approximates value functions across tasks, as well as to standard model-based and model-free algorithms. Across three exploratory experiments and a large preregistered experiment, our results provide strong evidence for a strategy that maps previously learned policies to novel scenarios. These results enrich our understanding of human reinforcement learning in complex environments with changing task demands.en-USJournal Article2023-02-0910.1101/815332