  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. It models
the return distribution using samples via a reparameterization technique widely
used in generative modeling and inference. We compare SDPG with the
state-of-art policy gradient method in DRL, distributed distributional
deterministic policy gradients (D4PG), which has demonstrated state-of-art
performance. We apply SDPG and D4PG to multiple OpenAI Gym environments and
observe that our algorithm shows better sample efficiency as well as higher
reward for most tasks.
