Most reinforcement learning (RL) methods focus on learning optimal policies
over low-level action spaces. While these methods can perform well in their
training environments, they lack the flexibility to transfer to new tasks.
Instead, RL agents that can act over useful, temporally extended skills rather
than low-level actions can learn new tasks more easily. Prior work in
skill-based RL either requires expert supervision to define useful skills,
which is hard to scale, or learns a skill-space from offline data with
heuristics that limit the adaptability of the skills, making them difficult to
transfer during downstream RL. Our approach, EXTRACT, instead utilizes
pre-trained vision language models to extract a discrete set of semantically
meaningful skills from offline data, each of which is parameterized by
continuous arguments, without human supervision. This skill parameterization
allows robots to learn new tasks by only needing to learn when to select a
specific skill and how to modify its arguments for the specific task. We
demonstrate through experiments in sparse-reward, image-based, robot
manipulation environments that EXTRACT can more quickly learn new tasks than
prior works, with major gains in sample efficiency and performance over prior
skill-based RL. Website at this https URL

使用预训练的视觉语言模型从离线数据中提取一组离散的语义相关技能，无需人工监督，通过参数化连续参数，使机器人能够学习新任务并提高样本效率和性能表现。

从离线数据提取可转移机器人技能的高效策略学习

EXTRACT: Efficient Policy Learning by Extracting Transferrable Robot  Skills from Offline Data

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。