Аннотация
Complex control tasks can often be solved by decomposing them into
hierarchies of manageable subtasks. Such decompositions require designers
to decide how much human knowledge should be used to help learn the
resulting components. On one hand, encoding human knowledge requires
manual effort and may incorrectly constrain the learner's hypothesis
space or guide it away from the best solutions. On the other hand,
it may make learning easier and enable the learner to tackle more
complex tasks. This article examines the impact of this trade-off
in tasks of varying difficulty. A space laid out by two dimensions
is explored: (1) how much human assistance is given and (2) how difficult
the task is. In particular, the neuroevolution learning algorithm
is enhanced with three different methods for learning the components
that result from a task decomposition. The first method, coevolution,
is mostly unassisted by human knowledge. The second method, layered
learning, is highly assisted. The third method, concurrent layered
learning, is a novel combination of the first two that attempts to
exploit human knowledge while retaining some of coevolution's flexibility.
Detailed empirical results are presented comparing and contrasting
these three approaches on two versions of a complex task, namely
robot soccer keepaway, that differ in difficulty of learning. These
results confirm that, given a suitable task decomposition, neuroevolution
can master difficult tasks. Furthermore, they demonstrate that the
appropriate level of human assistance depends critically on the difficulty
of the problem.
Пользователи данного ресурса
Пожалуйста,
войдите в систему, чтобы принять участие в дискуссии (добавить собственные рецензию, или комментарий)