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arXiv · 2110.10632

More Efficient Exploration with Symbolic Priors on Action Sequence Equivalences

Toby Johnstone·Nathan Grinsztajn·Johan Ferret·Philippe Preux
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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 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.

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Toby Johnstone, Nathan Grinsztajn, Johan Ferret, Philippe Preux. 2021-10-20. More Efficient Exploration with Symbolic Priors on Action Sequence Equivalences. https://arxiv.org/abs/2110.10632

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