Dynamical System Transfer Learning with Reduced Order Models

I have discussed previously the potential utility of applying Reinforcement Learning (RL) to analysis and control of complex physical systems. Developing RL algorithms to analyze or control complex dynamical systems faces one of the main challenges of many other RL problems — lengthy training times. This problem is complicated by the high computational cost of simulating complex physical dynamics. Compounding the thousands (perhaps millions) of RL training iterations needed for large state spaces and nonlinear systems with simulation times that may run into the hours (or even just minutes) per iteration can make problems intractable. Transfer learning has been used to reduce training times for RL problems and here we will explore application of transfer learning to RL for dynamical systems. Transfer learning relies on using models trained on similar problems to reduce training time with the assumption that the problem is similar enough that the model will have…