Enhanced Experience Replay Generation for Efficient Reinforcement Learning
This incremental improvement addresses efficiency in early training phases for real-time systems with sparse and slow data sampling.
The paper tackles slow data sampling in deep reinforcement learning for real systems by proposing an enhanced generative adversarial network (EGAN) to pre-train RL agents, resulting in a 20% faster training time initially and a 5% improvement over standard GAN pre-training with reduced variations.
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.