Before revision:  Inverse reinforcement learning (IRL) provides a framework to automatically acquire proper reward functions from expert demonstrations. Extending IRL to MFG , however, is challenging due to the complex notion of mean-field-type equilibria and the coupling between agent-level and population-level dynamics.
After revision:  Inverse reinforcement learning (IRL) holds promise for automatically acquiring suitable rewards from demonstrations, its extension to MFG is challenging due to the complex notion of mean-field-type equilibria and the coupling between agent-level and population-level dynamics.