  Characterizing aleatoric and epistemic uncertainty on the predicted rewards
can help in building reliable reinforcement learning (RL) systems. Aleatoric
uncertainty results from the irreducible environment stochasticity leading to
inherently risky states and actions. Epistemic uncertainty results from the
limited information accumulated during learning to make informed decisions.
Characterizing aleatoric and epistemic uncertainty can be used to speed up
learning in a training environment, improve generalization to similar testing
environments, and flag unfamiliar behavior in anomalous testing environments.
In this work, we introduce a framework for disentangling aleatoric and
epistemic uncertainty in RL. (1) We first define four desiderata that capture
the desired behavior for aleatoric and epistemic uncertainty estimation in RL
at both training and testing time. (2) We then present four RL models inspired
by supervised learning (i.e. Monte Carlo dropout, ensemble, deep kernel
learning models, and evidential networks) to instantiate aleatoric and
epistemic uncertainty. Finally, (3) we propose a practical evaluation method to
evaluate uncertainty estimation in model-free RL based on detection of
out-of-distribution environments and generalization to perturbed environments.
We present theoretical and experimental evidence to validate that carefully
equipping model-free RL agents with supervised learning uncertainty methods can
fulfill our desiderata.
