Skill Q-Network: Learning Adaptive Skill Ensemble for Mapless Navigation in Unknown Environments
<|reference_start|>Skill Q-Network: Learning Adaptive Skill Ensemble for Mapless Navigation in Unknown Environments: This paper focuses on the acquisition of mapless navigation skills within unknown environments. We introduce the Skill Q-Network (SQN), a novel reinforcement learning method featuring an adaptive skill ensemble mechanism. Unlike existing methods, our model concurrently learns a high-level skill decision process alongside multiple low-level navigation skills, all without the need for prior knowledge. Leveraging a tailored reward function for mapless navigation, the SQN is capable of learning adaptive maneuvers that incorporate both exploration and goal-directed skills, enabling effective navigation in new environments. Our experiments demonstrate that our SQN can effectively navigate complex environments, exhibiting a 40\% higher performance compared to baseline models. Without explicit guidance, SQN discovers how to combine low-level skill policies, showcasing both goal-directed navigations to reach destinations and exploration maneuvers to escape from local minimum regions in challenging scenarios. Remarkably, our adaptive skill ensemble method enables zero-shot transfer to out-of-distribution domains, characterized by unseen observations from non-convex obstacles or uneven, subterranean-like environments. The project page is available at https://sites.google.com/view/skill-q-net.<|reference_end|>
@article{seong2024skill,
    title={Skill Q-Network: Learning Adaptive Skill Ensemble for Mapless Navigation
  in Unknown Environments},
    author={Hyunki Seong, David Hyunchul Shim},
    journal={arXiv preprint arXiv:2403.16664},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16664},
    primaryClass={cs.RO}
}