Multi-objective dynamic programming with limited precision
This work addresses the computational challenge of handling exponential or infinite solution sets in multi-objective decision-making, which is incremental as it builds on an existing algorithm.
The paper tackles the problem of approximating the solution set for Multi-objective Markov Decision Processes, where solutions are often exponential or infinite, by proposing a limited precision dynamic programming method that yields a tractable number of solutions and experimentally shows they approximate the true Pareto front well.
This paper addresses the problem of approximating the set of all solutions for Multi-objective Markov Decision Processes. We show that in the vast majority of interesting cases, the number of solutions is exponential or even infinite. In order to overcome this difficulty we propose to approximate the set of all solutions by means of a limited precision approach based on White's multi-objective value-iteration dynamic programming algorithm. We prove that the number of calculated solutions is tractable and show experimentally that the solutions obtained are a good approximation of the true Pareto front.