title: Better state exploration using action sequence equivalence
abstract: 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 \emph{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.
authors: Nathan Grinsztajn Toby Johnstone Johan Ferret Philippe Preux
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