In this paper we consider the problem of robot navigation in simple maze-like
environments where the robot has to rely on its onboard sensors to perform the
navigation task. In particular, we are interested in solutions to this problem
that do not require localization, mapping or planning...
Additionally, we require
that our solution can quickly adapt to new situations (e.g., changing
navigation goals and environments). To meet these criteria we frame this
problem as a sequence of related reinforcement learning tasks. We propose a
successor feature based deep reinforcement learning algorithm that can learn to
transfer knowledge from previously mastered navigation tasks to new problem
instances. Our algorithm substantially decreases the required learning time
after the first task instance has been solved, which makes it easily adaptable
to changing environments. We validate our method in both simulated and real
robot experiments with a Robotino and compare it to a set of baseline methods
including classical planning-based navigation.