Least Squares Monte Carlo and Pathwise Optimization for Merchant Energy Production
研究了两种强化学习技术（最小二乘蒙特卡洛和路径优化）在能源生产公司运营决策中的应用，发现路径优化在最优性边界上更优但计算成本更高。
Modeling as real options the operations of energy production companies that operate in wholesale markets gives rise to a challenging Markov decision process. In “Least Squares Monte Carlo and Pathwise Optimization for Merchant Energy Production,” Yang, Nadarajah, and Secomandi study the performance of two reinforcement learning techniques that can be used to determine feasible operating policies and optimality bounds for this model, namely least squares Monte Carlo and pathwise optimization, extending the applicability of the latter method beyond optimal stopping by using principal component analysis and block coordinate descent. They find that both approaches lead to near optimal policies, but pathwise optimization outperforms least squares Monte Carlo in terms of dual bounds at the expense of more sizable computational requirements. These findings have potential relevance for managers of energy production assets that use analytics to optimize their operations and researchers interested in broadening the scope of pathwise optimization.