arXiv Open Access 2024

Utilizing Motion Matching with Deep Reinforcement Learning for Target Location Tasks

Jeongmin Lee Taesoo Kwon Hyunju Shin Yoonsang Lee

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Abstrak

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.

Topik & Kata Kunci

cs.GR

Penulis (4)

Jeongmin Lee

Taesoo Kwon

Hyunju Shin

Yoonsang Lee

Format Sitasi

APA MLA BibTeX

Lee, J., Kwon, T., Shin, H., Lee, Y. (2024). Utilizing Motion Matching with Deep Reinforcement Learning for Target Location Tasks. https://arxiv.org/abs/2403.15902

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Informasi Jurnal

Tahun Terbit: 2024
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓