  A common strategy to deal with the expensive reinforcement learning (RL) of
complex tasks is to decompose them into a collection of subtasks that are
usually simpler to learn as well as reusable for new problems. However, when a
robot learns the policies for these subtasks, common approaches treat every
policy learning process separately. Therefore, all these individual
(composable) policies need to be learned before tackling the learning process
of the complex task through policies composition. Moreover, such composition of
individual policies is usually performed sequentially, which is not suitable
for tasks that require to perform the subtasks concurrently. In this paper, we
propose to combine a set of composable Gaussian policies corresponding to these
subtasks using a set of activation vectors, resulting in a complex Gaussian
policy that is a function of the means and covariances matrices of the
composable policies. Moreover, we propose an algorithm for learning both
compound and composable policies within the same learning process by exploiting
the off-policy data generated from the compound policy. The algorithm is built
on a maximum entropy RL approach to favor exploration during the learning
process. The results of the experiments show that the experience collected with
the compound policy permits not only to solve the complex task but also to
obtain useful composable policies that successfully perform in their
corresponding subtasks.
