Learning-based techniques have become popular in both model predictive
control (MPC) and reinforcement learning (RL). Probabilistic ensemble (PE)
models offer a promising approach for modelling system dynamics, showcasing the
ability to capture uncertainty and scalability in high-dimensional control
scenarios. However, PE models are susceptible to mode collapse, resulting in
non-robust control when faced with environments slightly different from the
training set. In this paper, we introduce the
$\textbf{u}$ncertainty-$\textbf{d}$riven rob$\textbf{u}$st $\textbf{c}$ontrol
(UDUC) loss as an alternative objective for training PE models, drawing
inspiration from contrastive learning. We analyze the robustness of UDUC loss
through the lens of robust optimization and evaluate its performance on the
challenging Real-world Reinforcement Learning (RWRL) benchmark, which involves
significant environmental mismatches between the training and testing
environments.

通过引入基于不确定性驱动的鲁棒控制损失 (UDUC) 作为概率集合模型的替代目标，并受对比学习的启发，我们分析了 UDUC 损失的鲁棒性，并评估其在具有显著环境不匹配的挑战性实际强化学习基准 (RWRL) 上的性能。