  Incorporating prior knowledge in reinforcement learning algorithms is mainly
an open question. Even when insights about the environment dynamics are
available, reinforcement learning is traditionally used in a tabula rasa
setting and must explore and learn everything from scratch. In this paper, we
consider the problem of exploiting priors about action sequence equivalence:
that is, when different sequences of actions produce the same effect. We
propose a new local exploration strategy calibrated to minimize collisions and
maximize new state visitations. We show that this strategy can be computed at
little cost, by solving a convex optimization problem. By replacing the usual
epsilon-greedy strategy in a DQN, we demonstrate its potential in several
environments with various dynamic structures.
