Parameterized MDPs and Reinforcement Learning Problems -- A Maximum Entropy Principle Based Framework. (arXiv:2006.09646v3 [cs.LG] UPDATED)

We present a framework to address a class of sequential decision making
problems. Our framework features learning the optimal control policy with
robustness to noisy data, determining the unknown state and action parameters,
and performing sensitivity analysis with respect to problem parameters. We
consider two broad categories of sequential decision making problems modelled
as infinite horizon Markov Decision Processes (MDPs) with (and without) an
absorbing state. The central idea underlying our framework is to quantify
exploration in terms of the …

arxiv entropy framework learning reinforcement learning