Stochasticity mitigation in deployed ai agents

Abstract

The techniques disclosed herein mitigate stochasticity when controlling a mechanical system with artificial intelligence (AI) agents. In some configurations, AI agents are created using data generated by a machine learning model. Stochasticity is segmented temporally into near term and long term, and different strategies are used to address stochasticity in the different timeframes. For example, long term stochasticity may be addressed with changes to the reward function used to train the model. Short term stochasticity may be addressed by applying a margin to the output of an AI agent. Example margins include window averaging, clamps, and statistical process control bounds. In one configuration, AI agents are regression brains that are generated from setpoints inferred by the model from environmental states. The limitations inherent to fitting a regression line to this data may result in some predicted setpoints being outside of an allowed range.