Autonomous navigation using feature based hierarchical reinforcement learning

The ease of availability of low cost aerial platforms has given rise to extensive research in the field of autonomous navigation. There are strong indications in existing research that UAV autonomy leads to significant gains in terms of safety as well as performance in a number of scenarios, including but not limited to search and rescue missions in disaster areas. This paper tackles the problem of autonomous indoor navigation by applying reinforcement learning. In specific terms, this paper employs hierarchical reinforcement learning methods in order to overcome the challenges posed by the complexity and size of the problem space when dealing with navigational problems. In addition, a feature-based relative state is used so as to contain the number of states to a manageable level, while ensuring that the learnt policies are perfectly valid, independent of the size of the problem. The findings of this paper successfully demonstrate the ability of the Options method to solve a complex navigation problem involving goal finding, room exiting and battery recharging in an efficient manner, and further to solve a problem that cannot be solved using traditional flat Q-learning. In addition, this paper provides evidence of the positive influence of prior high level knowledge on the learning rate for a navigation problem as a) the problem size scales up and b) the problem complexity scales up.Aerospace EngineeringControl & OperationsControl & Simulatio