Wang, Zixing2022-02-152022-02-152021-12https://hdl.handle.net/11299/226342University of Minnesota M.S. thesis. 2021. Major: Computer Science. Advisor: Nikolaos Papanikolopoulos. 1 computer file (PDF); 33 pages.Reinforcement learning has been widely applied in exploration, navigation, manipulation, and other fields. Most of the relevant techniques generate kinematic commands (e.g., move, stop, turn) for agents based on the current state information. However, recent dense action representations based research, such as spatial action maps, pointing way-points to the agent in the same domain as its observation of the state shows great promise in mobile manipulation tasks. Inspired by that, we make the first step towards using a spatial action maps based method to effectively explore novel environmental spaces. To reduce the chance of redundant exploration, the visit frequency map (VFM) and its corresponding reward function are introduced to direct the agent to actively search previously unexplored areas. In the experimental section, our work was compared to the same method without VFM and the method based on traditional steering commands with the same input data in various environments. The results show conclusively that our method is more efficient than other methods.enExplorationNavigationReinforcement LearningState RepresentationThesis or Dissertation