  Classical reinforcement learning (RL) techniques are generally concerned with
the design of decision-making policies driven by the maximisation of the
expected outcome. Nevertheless, this approach does not take into consideration
the potential risk associated with the actions taken, which may be critical in
certain applications. To address that issue, the present research work
introduces a novel methodology based on distributional RL to derive sequential
decision-making policies that are sensitive to the risk, the latter being
modelled by the tail of the return probability distribution. The core idea is
to replace the $Q$ function generally standing at the core of learning schemes
in RL by another function taking into account both the expected return and the
risk. Named the risk-based utility function $U$, it can be extracted from the
random return distribution $Z$ naturally learnt by any distributional RL
algorithm. This enables to span the complete potential trade-off between risk
minimisation and expected return maximisation, in contrast to fully risk-averse
methodologies. Fundamentally, this research yields a truly practical and
accessible solution for learning risk-sensitive policies with minimal
modification to the distributional RL algorithm, and with an emphasis on the
interpretability of the resulting decision-making process.
