Estimating passive dynamics distributions in linearly solvable Markov decision processes from measured immediate costs in reinforcement learning problems
The Bellman equation in reinforcement learning for Markov decision processes becomes linear under specific conditions, where the passive dynamics is crucial but unknown in general. We propose a method to estimate it from observed immediate costs, by generating systems of linear equations.