  We study the robustness of deep reinforcement learning algorithms against
distribution shifts within contextual multi-stage stochastic combinatorial
optimization problems from the operations research domain. In this context,
risk-sensitive algorithms promise to learn robust policies. While this field is
of general interest to the reinforcement learning community, most studies
up-to-date focus on theoretical results rather than real-world performance.
With this work, we aim to bridge this gap by formally deriving a novel
risk-sensitive deep reinforcement learning algorithm while providing numerical
evidence for its efficacy. Specifically, we introduce discrete Soft
Actor-Critic for the entropic risk measure by deriving a version of the Bellman
equation for the respective Q-values. We establish a corresponding policy
improvement result and infer a practical algorithm. We introduce an environment
that represents typical contextual multi-stage stochastic combinatorial
optimization problems and perform numerical experiments to empirically validate
our algorithm's robustness against realistic distribution shifts, without
compromising performance on the training distribution. We show that our
algorithm is superior to risk-neutral Soft Actor-Critic as well as to two
benchmark approaches for robust deep reinforcement learning. Thereby, we
provide the first structured analysis on the robustness of reinforcement
learning under distribution shifts in the realm of contextual multi-stage
stochastic combinatorial optimization problems.
