  The use of Reinforcement Learning in real-world scenarios is strongly limited
by issues of scale. Most RL learning algorithms are unable to deal with
problems composed of hundreds or sometimes even dozens of possible actions, and
therefore cannot be applied to many real-world problems. We consider the RL
problem in the supervised classification framework where the optimal policy is
obtained through a multiclass classifier, the set of classes being the set of
actions of the problem. We introduce error-correcting output codes (ECOCs) in
this setting and propose two new methods for reducing complexity when using
rollouts-based approaches. The first method consists in using an ECOC-based
classifier as the multiclass classifier, reducing the learning complexity from
O(A2) to O(Alog(A)). We then propose a novel method that profits from the
ECOC's coding dictionary to split the initial MDP into O(log(A)) seperate
two-action MDPs. This second method reduces learning complexity even further,
from O(A2) to O(log(A)), thus rendering problems with large action sets
tractable. We finish by experimentally demonstrating the advantages of our
approach on a set of benchmark problems, both in speed and performance.
