Continuous Control Reinforcement Learning: Distributed Distributional
  DrQ Algorithms
Zehao Zhou
Distributed Distributional DrQ is a model-free and off-policy RL algorithm
for continuous control tasks based on the state and observation of the agent,
which is an actor-critic method with the data-augmentation and the
distributional perspective of critic value function. Aim to learn to control
the agent and master some tasks in a high-dimensional continuous space. DrQ-v2
uses DDPG as the backbone and achieves out-performance in various continuous
control tasks. Here Distributed Distributional DrQ uses Distributed
Distributional DDPG as the backbone, and this modification aims to achieve
better performance in some hard continuous control tasks through the better
expression ability of distributional value function and distributed actor
policies.