2016
Model-based reinforcement learning with parametrized physical models and optimism-driven exploration
ICRA 2016
In this paper, we present a robotic model-based reinforcement learning method that combines ideas from model identification and model predictive control. We use a feature-based representation of the dynamics that allows the dynamics model to be fitted with a simple least squares procedure, and the f