Learning the optimal state-feedback via supervised imitation learning

Understand

Imitation learning is a control design paradigm that seeks to learn a control policy reproducing demonstrations from expert agents.

• By substituting expert demonstrations for optimal behaviours, the same paradigm leads to the design of control policies closely approximating the optimal state-feedback.
• This approach requires training a machine learning algorithm (in our case deep neural networks) directly on state-control pairs originating from optimal trajectories.
• We have shown in previous work that, when restricted to low-dimensional state and control spaces, this approach is very successful in several deterministic, non-linear problems in continuous-time.