3 papers
cs.AI2020
Flexible and Efficient Long-Range Planning Through Curious Exploration
Aidan Curtis, Minjian Xin, Dilip Arumugam +2
Identifying algorithms that flexibly and efficiently discover temporally-extended multi-phase plans is an essential step for the advancement of robotics and model-based reinforceme…
cs.LG2017
Modular Continual Learning in a Unified Visual Environment
Kevin T. Feigelis, Blue Sheffer, Daniel L. K. Yamins
A core aspect of human intelligence is the ability to learn new tasks quickly and switch between them flexibly. Here, we describe a modular continual reinforcement learning paradig…
cs.LG2017
A Useful Motif for Flexible Task Learning in an Embodied Two-Dimensional Visual Environment
Kevin T. Feigelis, Daniel L. K. Yamins
Animals (especially humans) have an amazing ability to learn new tasks quickly, and switch between them flexibly. How brains support this ability is largely unknown, both neuroscie…