  Reinforcement Learning (RL) is emerging as tool for tackling complex control
and decision-making problems. However, in high-risk environments such as
healthcare, manufacturing, automotive or aerospace, it is often challenging to
bridge the gap between an apparently optimal policy learnt by an agent and its
real-world deployment, due to the uncertainties and risk associated with it.
Broadly speaking RL agents face two kinds of uncertainty, 1. aleatoric
uncertainty, which reflects randomness or noise in the dynamics of the world,
and 2. epistemic uncertainty, which reflects the bounded knowledge of the agent
due to model limitations and finite amount of information/data the agent has
acquired about the world. These two types of uncertainty carry fundamentally
different implications for the evaluation of performance and the level of risk
or trust. Yet these aleatoric and epistemic uncertainties are generally
confounded as standard and even distributional RL is agnostic to this
difference. Here we propose how a distributional approach (UA-DQN) can be
recast to render uncertainties by decomposing the net effects of each
uncertainty. We demonstrate the operation of this method in grid world examples
to build intuition and then show a proof of concept application for an RL agent
operating as a clinical decision support system in critical care
