Sample-based Distributional Policy Gradient

Understand

Distributional reinforcement learning (DRL) is a recent reinforcement learning framework whose success has been supported by various empirical studies.

• It relies on the key idea of replacing the expected return with the return distribution, which captures the intrinsic randomness of the long term rewards.
• Most of the existing literature on DRL focuses on problems with discrete action space and value based methods.
• In this work, motivated by applications in robotics with continuous action space control settings, we propose sample-based distributional policy gradient (SDPG) algorithm.