  In many risk-aware and multi-objective reinforcement learning settings, the
utility of the user is derived from the single execution of a policy. In these
settings, making decisions based on the average future returns is not suitable.
For example, in a medical setting a patient may only have one opportunity to
treat their illness. When making a decision, just the expected return -- known
in reinforcement learning as the value -- cannot account for the potential
range of adverse or positive outcomes a decision may have. Our key insight is
that we should use the distribution over expected future returns differently to
represent the critical information that the agent requires at decision time. In
this paper, we propose Distributional Monte Carlo Tree Search, an algorithm
that learns a posterior distribution over the utility of the different possible
returns attainable from individual policy executions, resulting in good
policies for both risk-aware and multi-objective settings. Moreover, our
algorithm outperforms the state-of-the-art in multi-objective reinforcement
learning for the expected utility of the returns.
