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.

翻译：我们提出一种方法，利用深度强化学习（DRL）直接为长期任务生成运动匹配查询，尤其侧重于到达特定位置。通过整合运动匹配与DRL，我们的方法在标准台式机上只需几分钟即可快速学习针对目标定位任务的策略，并采用简洁的奖励设计。此外，我们提出了一种独特的命中奖励与障碍物课程机制，以增强在存在移动障碍物的环境中的策略学习效果。