Systems and methods for risk-sensitive reinforcement learning are disclosed. In one embodiment, a method for a method for training a risk-sensitive reinforcement learning policy may include: (1) receiving, from a data source, a plurality of sets of training data for a plurality of time steps; (2) receiving a training budget comprising a plurality of episodes, a risk aversion coefficient, and an end state; and (3) calculating a correction factor using the training data. The correction factor may minimize stochasticity based on a risk aversion coefficient.
SYSTEMS AND METHODS FOR RISK-SENSITIVE REINFORCEMENT LEARNING