Structure for Efficient Reinforcement Learning (SERL)
In real-world situations that involve many stimuli and actions, algorithms that make limited assumptions about the environment can learn extremely slowly, exposing a crucial weakness in comparison to animal and human learning. One reason for this discrepancy is that humans and animals take advantage of structure that is inherent in the world and use this structure to simplify learning and exploration.
As both humans and artificial agents regularly face tasks with latent structure, the use of structure is important for both understanding human/animal learning as well as designing artificial agents. This interdisciplinary workshop explores the crucial roles structure plays for both humans and artificial reinforcement learning agents: how can they benefit by learning latent structure, and what insight can we gain about this computionational problem through empirical research?
The workshop will include a series of short talks by invited speakers followed by a group discussion.
Location: Montréal, Canada
Date/Time: Wednesday July 10, from 1pm to 5pm.