We study the problem of Distributionally Robust Constrained RL (DRC-RL),
where the goal is to maximize the expected reward subject to environmental
distribution shifts and constraints. This setting captures situations where
training and testing environments differ, and policies must satisfy constraints
motivated by safety or limited budgets. Despite significant progress toward
algorithm design for the separate problems of distributionally robust RL and
constrained RL, there do not yet exist algorithms with end-to-end convergence
guarantees for DRC-RL. We develop an algorithmic framework based on strong
duality that enables the first efficient and provable solution in a class of
environmental uncertainties. Further, our framework exposes an inherent
structure of DRC-RL that arises from the combination of distributional
robustness and constraints, which prevents a popular class of iterative methods
from tractably solving DRC-RL, despite such frameworks being applicable for
each of distributionally robust RL and constrained RL individually. Finally, we
conduct experiments on a car racing benchmark to evaluate the effectiveness of
the proposed algorithm.

本研究通过基于强对偶性的算法框架，在环境不确定性的一个类别中提出了第一个高效且可证明的解决方法，来解决分布鲁棒受限强化学习（DRC-RL）问题，该问题旨在最大化预期奖励，同时受制于环境分布变化和约束条件。