Utilizing Motion Matching with Deep Reinforcement Learning for Target Location Tasks
<|reference_start|>Utilizing Motion Matching with Deep Reinforcement Learning for Target Location Tasks: We present an approach using deep reinforcement learning (DRL) to directly generate motion matching queries for long-term tasks, particularly targeting the reaching of specific locations. By integrating motion matching and DRL, our method demonstrates the rapid learning of policies for target location tasks within minutes on a standard desktop, employing a simple reward design. Additionally, we propose a unique hit reward and obstacle curriculum scheme to enhance policy learning in environments with moving obstacles.<|reference_end|>
@article{lee2024utilizing,
    title={Utilizing Motion Matching with Deep Reinforcement Learning for Target
  Location Tasks},
    author={Jeongmin Lee, Taesoo Kwon, Hyunju Shin, Yoonsang Lee},
    journal={arXiv preprint arXiv:2403.15902},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15902},
    primaryClass={cs.GR}
}