Learning-based vehicle planning is receiving increasing attention with the
emergence of diverse driving simulators and large-scale driving datasets. While
offline reinforcement learning (RL) is well suited for these safety-critical
tasks, it still struggles to plan over extended periods. In this work, we
present a skill-based framework that enhances offline RL to overcome the
long-horizon vehicle planning challenge. Specifically, we design a variational
autoencoder (VAE) to learn skills from offline demonstrations. To mitigate
posterior collapse of common VAEs, we introduce a two-branch sequence encoder
to capture both discrete options and continuous variations of the complex
driving skills. The final policy treats learned skills as actions and can be
trained by any off-the-shelf offline RL algorithms. This facilitates a shift in
focus from per-step actions to temporally extended skills, thereby enabling
long-term reasoning into the future. Extensive results on CARLA prove that our
model consistently outperforms strong baselines at both training and new
scenarios. Additional visualizations and experiments demonstrate the
interpretability and transferability of extracted skills.

通过设计变分自编码器来学习技能，我们提出了一个基于技能的框架来增强离线强化学习，从而克服车辆规划中的长期规划挑战。通过将学到的技能作为动作，可以训练出能够进行长期推理和规划未来的最终策略，从而在 CARLA 上取得了强大的性能优势。