Reinforcement learning via offline trajectory planning based on iteratively approximated models

In this paper we use artificial neural networks (ANN) to approximate the dynamics of nonlinear (mechanical) systems. These iteratively approximated neural system models are used in an offline trajectory planning to calculate an optimal feedback law that is applied to the real system. This model-based reinforcement learning (RL) approach is evaluated on the swing-up manoeuvre of the cart-pole system and shows a significant performance gain in terms of data efficiency compared to model-free RL approaches. Furthermore, we show experimental results on a test bench. The proposed algorithm is capable of approximating an optimal feedback law for the system after only a few trials.