Control-Oriented Scenario Tree Construction through Reinforcement Learning
The episode discusses a paper proposing a reinforcement learning method to build scenario trees for stochastic control, optimizing for closed-loop profit rather than distributional accuracy. The hosts highlight its success in battery arbitrage, beating classical reduction methods and improving tail risk, with compact trees and better worst-case profits.