Enhanced Experience Replay Generation for Efficient Reinforcement Learning

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 learning, compared to no pre-training, and an improvement compared to training with GAN by about 5 and slow data sampling the EGAN could be used to speed up the early phases of the training process.