Multi-objective dynamic programming with limited precision
Copyright policy )Multi-objective dynamic programming with limited precision
AbstractThis 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.
- University of Malaga Spain
dynamic programming, FOS: Computer and information sciences, reinforcement learning, Computer Science - Machine Learning, Multi-objective, 330, Markov and semi-Markov decision processes, I.2.8, Machine Learning (stat.ML), Dynamic programming, F.2.2; I.2.8, 004, Markov decision processes, Machine Learning (cs.LG), multi-objective, Programación dinámica, Statistics - Machine Learning, Reinforcement learning, F.2.2, Multi-objective and goal programming
dynamic programming, FOS: Computer and information sciences, reinforcement learning, Computer Science - Machine Learning, Multi-objective, 330, Markov and semi-Markov decision processes, I.2.8, Machine Learning (stat.ML), Dynamic programming, F.2.2; I.2.8, 004, Markov decision processes, Machine Learning (cs.LG), multi-objective, Programación dinámica, Statistics - Machine Learning, Reinforcement learning, F.2.2, Multi-objective and goal programming
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