3 research outputs found
Self-Attentional Credit Assignment for Transfer in Reinforcement Learning
The ability to transfer knowledge to novel environments and tasks is a
sensible desiderata for general learning agents. Despite the apparent promises,
transfer in RL is still an open and little exploited research area. In this
paper, we take a brand-new perspective about transfer: we suggest that the
ability to assign credit unveils structural invariants in the tasks that can be
transferred to make RL more sample-efficient. Our main contribution is SECRET,
a novel approach to transfer learning for RL that uses a backward-view credit
assignment mechanism based on a self-attentive architecture. Two aspects are
key to its generality: it learns to assign credit as a separate offline
supervised process and exclusively modifies the reward function. Consequently,
it can be supplemented by transfer methods that do not modify the reward
function and it can be plugged on top of any RL algorithm.Comment: 21 pages, 10 figures, 3 tables (accepted as an oral presentation at
the Learning Transferable Skills workshop, NeurIPS 2019