  Modern navigation algorithms based on deep reinforcement learning (RL) show
promising efficiency and robustness. However, most deep RL algorithms operate
in a risk-neutral manner, making no special attempt to shield users from
relatively rare but serious outcomes, even if such shielding might cause little
loss of performance. Furthermore, such algorithms typically make no provisions
to ensure safety in the presence of inaccuracies in the models on which they
were trained, beyond adding a cost-of-collision and some domain randomization
while training, in spite of the formidable complexity of the environments in
which they operate. In this paper, we present a novel distributional RL
algorithm that not only learns an uncertainty-aware policy, but can also change
its risk measure without expensive fine-tuning or retraining. Our method shows
superior performance and safety over baselines in partially-observed navigation
tasks. We also demonstrate that agents trained using our method can adapt their
policies to a wide range of risk measures at run-time.
