Posterior Sampling for Deep Reinforcement Learning

Remo Sasso, Michelangelo Conserva, Paulo Rauber

Despite remarkable successes, deep reinforcement learning algorithms remain sample inefficient: they require an enormous amount of trial and error to find good policies. Model-base…