Recent years have witnessed significant progresses in deep Reinforcement
Learning (RL). Empowered with large scale neural networks, carefully designed
architectures, novel training algorithms and massively parallel computing
devices, researchers are able to attack many challenging RL problems. However,
in machine learning, more training power comes with a po