2023
Posterior Sampling for Deep Reinforcement Learning
ICML 2023poster
Despite remarkable successes, deep reinforcement learning algorithms remain sample inefficient: they require an enormous amount of trial and error to find good policies. Model-based algorithms promise sample efficiency by building an environment model that can be used for planning. Posterior Samplin…