The goal of robust reinforcement learning (RL) is to learn a policy that is
robust against the uncertainty in model parameters. Parameter uncertainty
commonly occurs in many real-world RL applications due to simulator modeling
errors, changes in the real-world system dynamics over time, and adversarial
disturbances. Robust RL is typically formulated as a max-min problem, where the
objective is to learn the policy that maximizes the value against the worst
possible models that lie in an uncertainty set. In this work, we propose a
robust RL algorithm called Robust Fitted Q-Iteration (RFQI), which uses only an
offline dataset to learn the optimal robust policy. Robust RL with offline data
is significantly more challenging than its non-robust counterpart because of
the minimization over all models present in the robust Bellman operator. This
poses challenges in offline data collection, optimization over the models, and
unbiased estimation. In this work, we propose a systematic approach to overcome
these challenges, resulting in our RFQI algorithm. We prove that RFQI learns a
near-optimal robust policy under standard assumptions and demonstrate its
superior performance on standard benchmark problems.

该论文提出一种名为 RFQI 的稳健强化学习算法，使用离线数据集来优化策略，在标准条件下该算法能够学习到近乎最优的稳健策略，并且在标准基准测试问题上展现出卓越的性能表现。