Mind the Uncertainty: Risk-Aware and Actively Exploring Model-Based
  Reinforcement Learning
Marin Vlastelica Sebastian Blaes Cristina Pineri Georg Martius
We introduce a simple but effective method for managing risk in model-based
reinforcement learning with trajectory sampling that involves probabilistic
safety constraints and balancing of optimism in the face of epistemic
uncertainty and pessimism in the face of aleatoric uncertainty of an ensemble
of stochastic neural networks.Various experiments indicate that the separation
of uncertainties is essential to performing well with data-driven MPC
approaches in uncertain and safety-critical control environments.