Ergodic Annealing
Artificial Intelligence
2020-08-04 v1 Theoretical Economics
Probability
Machine Learning
Abstract
Simulated Annealing is the crowning glory of Markov Chain Monte Carlo Methods for the solution of NP-hard optimization problems in which the cost function is known. Here, by replacing the Metropolis engine of Simulated Annealing with a reinforcement learning variation -- that we call Macau Algorithm -- we show that the Simulated Annealing heuristic can be very effective also when the cost function is unknown and has to be learned by an artificial agent.
Keywords
Cite
@article{arxiv.2008.00234,
title = {Ergodic Annealing},
author = {Carlo Baldassi and Fabio Maccheroni and Massimo Marinacci and Marco Pirazzini},
journal= {arXiv preprint arXiv:2008.00234},
year = {2020}
}