Ergodic Annealing

• Tác giả: Carlo Baldassi, Fabio Maccheroni, Massimo Marinacci, Marco Pirazzini
• Ngôn ngữ: eng
• Ký hiệu phân loại: 536.73 Entropy
• Thông tin xuất bản: 2020
• Bộ sưu tập: Metadata
• ID: 164952

Tóm tắt

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.