Reinforcement Learning Optimizes Time-Split Risk Metric

Roberto Daluiso (arXiv preprint, Feb. 12, 2026) proposes a new risk metric for reinforcement learning that targets the time split of total returns rather than aggregate return risk. The paper analyzes properties of the objective, generalizes learning algorithms to optimize it, and reports numerical results on toy examples, noting relevance to hedging and other sequential finance problems.