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The Cartographer
Ergodic Annealing
August 01, 2020 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Carlo Baldassi, Fabio Maccheroni, Massimo Marinacci, Marco Pirazzini
arXiv ID
2008.00234
Category
cs.AI: Artificial Intelligence
Cross-listed
econ.TH,
math.PR,
stat.ML
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
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.
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