Minimum information divergence of Q-functions for dynamic treatment resumes. (arXiv:2211.08741v1 [stat.ME])

This paper aims at presenting a new application of information geometry to
reinforcement learning focusing on dynamic treatment resumes. In a standard
framework of reinforcement learning, a Q-function is defined as the conditional
expectation of a reward given a state and an action for a single-stage
situation. We introduce an equivalence relation, called the policy equivalence,
in the space of all the Q-functions. A class of information divergence is
defined in the Q-function space for every stage. The main objective …

arxiv divergence information resumes treatment