  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.
