Systems and methods for inverse constraint reinforcement from a mixture of experts

A system for inferring constraints for reinforcement learning of 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 sequences of the expert state-action pairs including information about a given state of the environment and a corresponding action that is to be executed in response to the given state;
• partition the set of expert trajectories into one or more subsets of expert trajectories by employing a trained neural network, each subset being associated with one of the agents;
• and
• determine, for each agent, an agent constraint based on the corresponding subset of expert trajectories.