Heterogeneous multi-player imitation learning

This paper studies imitation learning in nonlinear multi-player game systems with heterogeneous control input dynamics. We propose a model-free data-driven inverse reinforcement learning (RL) algorithm for a leaner to find the cost functions of a N -player Nash expert system given the expert’s states and control inputs. This allows us to address the imitation learning problem without prior knowledge of the expert’s system dynamics. To achieve this, we provide a basic model-based algorithm that is built upon RL and inverse optimal control. This serves as the foundation for our final model-free inverse RL algorithm which is implemented via neural network-based value function approximators. Theoretical analysis and simulation examples verify the methods.