  Applying deep reinforcement learning (RL) on real systems suffers from slow
data sampling. We propose an enhanced generative adversarial network (EGAN) to
initialize an RL agent in order to achieve faster learning. The EGAN utilizes
the relation between states and actions to enhance the quality of data samples
generated by a GAN. Pre-training the agent with the EGAN shows a steeper
learning curve with a 20% improvement of training time in the beginning of
learning, compared to no pre-training, and an improvement compared to training
with GAN by about 5% with smaller variations. For real time systems with sparse
and slow data sampling the EGAN could be used to speed up the early phases of
the training process.
