Systems and methods for executing confidence-aware reinforcement learning

A system for executing confidence-aware reinforcement learning for an Artificial Intelligence (AI) model for subsequent deployment of that AI model in an environment, the system comprising a controller and a memory storing a plurality of executable instructions which, when executed by the controller, cause the system to:

• access a set of expert trajectories, each expert trajectory comprising a sequence of expert state-action pairs, a given one of the expert trajectories including information about a given state of the environment and a corresponding action that is to be executed in response to the given state, the expert entities complying with an expert constraint that is unknown;
• generate a main constraint for the set of expert trajectories, the main constraint being conditioned on a pre-determined confidence level, the pre-determined confidence level being indicative of a probability that the main constraint is at least as constraining as the expert constraint, the main constraint comprising one or more rules limiting the actions that are executable by the AI model;
• determine a target policy among a plurality of policies, the target policy complying with the main constraint;
• and
• execute the target policy by the AI model.