Accelerating deep reinforcement learning via knowledge-guided policy network

Deep reinforcement learning has contributed to dramatic advances in many tasks, such as playing games, controlling robots, and navigating complex environments. However, it requires many interactions with the environment. This is different from the human learning process since humans can use prior knowledge, which can significantly speed up the learning process as it avoids unnecessary exploration. Previous works integrating knowledge in RL did not model uncertainty in human cognition, which reduces the reliability of knowledge. In this paper, we propose a knowledge-guided policy network, a novel framework that combines suboptimal human knowledge with reinforcement learning. Our framework consists of a fuzzy rule controller representing human knowledge and a refined module to fine-tune suboptimal prior knowledge. The proposed framework is end-to-end and can be combined with existing reinforcement learning algorithms such as PPO, AC, and SAC. We conduct experiments on both discrete and continuous control tasks. The empirical results show that our approach, which combines suboptimal human knowledge and RL, significantly improves the learning efficiency of basic RL algorithms, even with very low-performance human prior knowledge. Additional experiments are conducted on the number of fuzzy rules and the interpretability of the policy, which make our proposed framework more complete and reasonable. The code for this research is released under the project page of https://github.com/yuyuanq/reinforcement-learning-using-knowledge-controller.