Skill-based reinforcement learning (RL) has emerged as a promising strategy
to leverage prior knowledge for accelerated robot learning. Skills are
typically extracted from expert demonstrations and are embedded into a latent
space from which they can be sampled as actions by a high-level RL agent.
However, this skill space is expansive, and not all skills are relevant for a
given robot state, making exploration difficult. Furthermore, the downstream RL
agent is limited to learning structurally similar tasks to those used to
construct the skill space. We firstly propose accelerating exploration in the
skill space using state-conditioned generative models to directly bias the
high-level agent towards only sampling skills relevant to a given state based
on prior experience. Next, we propose a low-level residual policy for
fine-grained skill adaptation enabling downstream RL agents to adapt to unseen
task variations. Finally, we validate our approach across four challenging
manipulation tasks that differ from those used to build the skill space,
demonstrating our ability to learn across task variations while significantly
accelerating exploration, outperforming prior works. Code and videos are
available on our project website: this https URL.

通过使用状态条件生成模型在技能空间中加速探索，同时提出低层次的剩余策略来适应未知的任务变化，从而在与先前工作的比较中显着加快了探索速度，并跨四个不同于那些用于构建技能空间的具有挑战性的操作任务进行了验证。