Distributional Reinforcement Learning for Efficient Exploration

Understand

In distributional reinforcement learning (RL), the estimated distribution of value function models both the parametric and intrinsic uncertainties.

• We propose a novel and efficient exploration method for deep RL that has two components.
• The first is a decaying schedule to suppress the intrinsic uncertainty.
• The second is an exploration bonus calculated from the upper quantiles of the learned distribution.