no code implementations • 1 Jan 2021 • Martin A Bertran, Guillermo Sapiro, Mariano Phielipp
Deep Reinforcement Learning (DRL) can distill behavioural policies from sensory input that solve complex tasks, however, the policies tend to be task-specific and sample inefficient, requiring a large number of interactions with the environment that may be costly or impractical for many real world applications.
