Randomized Prior Functions for Deep Reinforcement Learning

Understand

Dealing with uncertainty is essential for efficient reinforcement learning.

• There is a growing literature on uncertainty estimation for deep learning from fixed datasets, but many of the most popular approaches are poorly-suited to sequential decision problems.
• Other methods, such as bootstrap sampling, have no mechanism for uncertainty that does not come from the observed data.
• We highlight why this can be a crucial shortcoming and propose a simple remedy through addition of a randomized untrainable `prior' network to each ensemble member.