Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning
Michael Lutter,Johannes Silberbauer,Joe Watson,Jan Peters,Michael Lutter,Johannes Silberbauer,Joe Watson,Jan Peters
A limitation of model-based reinforcement learning (MBRL) is the exploitation of errors in the learned models. Blackbox models can fit complex dynamics with high fidelity, but their behavior is undefined outside of the data distribution. Physics-based models are better at extrapolating, due to the general validity of their informed structure, but underfit in the real world due to the presence of u...
