Robust Entropy-regularized Markov Decision Processes. (arXiv:2112.15364v1 [cs.LG])

Stochastic and soft optimal policies resulting from entropy-regularized
Markov decision processes (ER-MDP) are desirable for exploration and imitation
learning applications. Motivated by the fact that such policies are sensitive
with respect to the state transition probabilities, and the estimation of these
probabilities may be inaccurate, we study a robust version of the ER-MDP model,
where the stochastic optimal policies are required to be robust with respect to
the ambiguity in the underlying transition probabilities. Our work is at the
crossroads …

arxiv decision entropy markov processes