Towards Generalization and Efficiency in Reinforcement Learning

GHC 8102

Abstract: In classic supervised machine learning, a learning agent behaves as a passive observer: it receives examples from some external environment which it has no control over and then makes predictions. Reinforcement Learning (RL), on the other hand, is fundamentally interactive: an autonomous agent must learn how to behave in an unknown and possibly hostile [...]