Safe Wasserstein Constrained Deep Q-Learning

7 Feb 2020 Aaron Kandel Scott J. Moura

This paper presents a distributionally robust Q-Learning algorithm (DrQ) which leverages Wasserstein ambiguity sets to provide probabilistic out-of-sample safety guarantees during online learning. First, we follow past work by separating the constraint functions from the principal objective to create a hierarchy of machines which estimate the feasible state-action space within the constrained Markov decision process (CMDP)... (read more)

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