Physics-Informed Model-Based Reinforcement Learning
Published at Learning for Dynamics & Control Conference (L4DC), 2023. We learn the dynamics model of a robot using a physics-informed neural network and use it to train a model-based RL algorithm. We show that, in model-based RL, model accuracy mainly matters in environments that are sensitive to initial conditions, where numerical errors accumulate fast.
