Machine learned models from simulation data

This research proposal aims to develop a constraint learning model rooted on principals of inverse reinforcement learning (IRL) to be referred to as a “nano-framework”. This nano-framework is intended to use a potential field approach to replicate small-scale environmental perception. The developed potential field would then be used solely by the decision-making modules of the model improving overall computational performance compared to traditional perception techniques.