A Local Path Planning Method Based on Q-Learning

Q-learning belongs to reinforcement learning and artificial intelligence learning algorithm. Reinforcement learning does not need external guidance; it interacts with the external environment through its own sensors. It maps the state of the external input environment to output action through continuous learning, and makes the corresponding reward value of this action the maxi-mum. In order to make the submersible have the ability to adapt to the environment independently, it can adjust the path automatically through its own learning. This paper proposes to introduce Q-learning mechanism in reinforcement learning to complete the adjustment of fuzzy rule strategy in un-known environment.