Learning Complex Motions by Sequencing Simpler Motion Templates
G. Neumann, W. Maass, and J. Peters
Abstraction of complex, longer motor tasks into simpler elemental movements
enables humans and animals to exhibit motor skills which have not yet been
matched by robots. Humans intuitively decompose complex motions into smaller,
simpler segments. For example when describing simple movements like drawing a
triangle with a pen, we can easily name the basic steps of this movement.
Surprisingly, such abstractions have rarely been used in artificial motor
skill learning algorithms. These algorithms typically choose a new action
(such as a torque or a force) at a very fast time-scale. As a result, both
policy and temporal credit assignment problem become unnecessarily complex -
often beyond the reach of current machine learning methods. We introduce a
new framework for temporal abstractions in reinforcement learning (RL), i.e.
RL with motion templates. We present a new algorithm for this framework which
can learn high-quality policies by making only few abstract decisions.
Reference: G. Neumann, W. Maass, and J. Peters.
Learning complex motions by sequencing simpler motion templates.
In Proc. of the Int. Conf. on Machine Learning (ICML 2009), Montreal,