论文标题
带贡式退火
Ergodic Annealing
论文作者
论文摘要
模拟退火是马尔可夫链蒙特卡洛方法的冠状荣耀，用于解决已知成本函数的NP-硬化优化问题。在这里，通过用强化学习变化代替模拟退火的大都市引擎（我们称为澳门算法），我们表明，当成本函数未知并且必须由人工制剂学习时，模拟的退火启发式也可以非常有效。
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.
