  This paper addresses the design and implementation of complex Reinforcement
Learning (RL) behaviors where multi-dimensional action spaces are involved, as
well as the need to execute the behaviors in real-time using robotic platforms
with limited computational resources and training times. For this purpose, we
propose the use of decentralized RL, in combination with finite support basis
functions as alternatives to Gaussian RBF, in order to alleviate the effects of
the curse of dimensionality on the action and state spaces respectively, and to
reduce the computation time. As testbed, a RL based controller for the in-walk
kick in NAO robots, a challenging and critical problem for soccer robotics, is
used. The reported experiments show empirically that our solution saves up to
99.94% of execution time and 98.82% of memory consumption during execution,
without diminishing performance compared to classical approaches.
