  Efficient and effective exploration in continuous space is a central problem
in applying reinforcement learning (RL) to autonomous driving. Skills learned
from expert demonstrations or designed for specific tasks can benefit the
exploration, but they are usually costly-collected, unbalanced/sub-optimal, or
failing to transfer to diverse tasks. However, human drivers can adapt to
varied driving tasks without demonstrations by taking efficient and structural
explorations in the entire skill space rather than a limited space with
task-specific skills. Inspired by the above fact, we propose an RL algorithm
exploring all feasible motion skills instead of a limited set of task-specific
and object-centric skills. Without demonstrations, our method can still perform
well in diverse tasks. First, we build a task-agnostic and ego-centric (TaEc)
motion skill library in a pure motion perspective, which is diverse enough to
be reusable in different complex tasks. The motion skills are then encoded into
a low-dimension latent skill space, in which RL can do exploration efficiently.
Validations in various challenging driving scenarios demonstrate that our
proposed method, TaEc-RL, outperforms its counterparts significantly in
learning efficiency and task performance.
