PT  - JOURNAL ARTICLE
AU  - Momchil S. Tomov
AU  - Eric Schulz
AU  - Samuel J. Gershman
TI  - Multi-Task Reinforcement Learning in Humans
AID  - 10.1101/815332
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 815332
4099  - http://biorxiv.org/content/early/2019/10/22/815332.short
4100  - http://biorxiv.org/content/early/2019/10/22/815332.full
AB  - The 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.