  We present RL-GAN-Net, where a reinforcement learning (RL) agent provides
fast and robust control of a generative adversarial network (GAN). Our
framework is applied to point cloud shape completion that converts noisy,
partial point cloud data into a high-fidelity completed shape by controlling
the GAN. While a GAN is unstable and hard to train, we circumvent the problem
by (1) training the GAN on the latent space representation whose dimension is
reduced compared to the raw point cloud input and (2) using an RL agent to find
the correct input to the GAN to generate the latent space representation of the
shape that best fits the current input of incomplete point cloud. The suggested
pipeline robustly completes point cloud with large missing regions. To the best
of our knowledge, this is the first attempt to train an RL agent to control the
GAN, which effectively learns the highly nonlinear mapping from the input noise
of the GAN to the latent space of point cloud. The RL agent replaces the need
for complex optimization and consequently makes our technique real time.
Additionally, we demonstrate that our pipelines can be used to enhance the
classification accuracy of point cloud with missing data.
