对比多种强化学习算法在传感器失效环境下的导航表现，发现DreamerV3更稳健。
Benchmarking Deep Reinforcement Learning for Navigation in Denied Sensor Environments
- 构建可配置传感器干扰的导航基准测试，评估不同算法鲁棒性。
- DreamerV3在动态目标视觉导航中表现最优，其他方法无法学习该任务。
- 对抗训练提升传感器缺失场景表现，但牺牲常规环境性能。
深度强化学习（DRL）被用于未知环境中的自主导航。现有研究多假设传感器数据完美，但真实环境常存在自然与人为噪声及信号中断。本文构建了一个可配置传感器干扰的导航基准，评估主流与新兴DRL算法的表现。重点比较了无模型PPO与基于模型的DreamerV3在传感器受限条件下的差异。结果表明，DreamerV3在动态目标视觉端到端导航任务中显著优于其他方法，且其他算法无法完成该任务；在各类传感器缺失场景下，DreamerV3整体表现更优。为提升鲁棒性，引入对抗训练，虽在传感器正常环境上性能略有下降，但在干扰环境中效果显著改善。本工作为开发应对不确定与传感器失效的先进导航策略提供了起点。
原文摘要 · Abstract (English)
Deep Reinforcement learning (DRL) is used to enable autonomous navigation in unknown environments. Most research assume perfect sensor data, but real-world environments may contain natural and artificial sensor noise and denial. Here, we present a benchmark of both well-used and emerging DRL algorithms in a navigation task with configurable sensor denial effects. In particular, we are interested in comparing how different DRL methods (e.g. model-free PPO vs. model-based DreamerV3) are affected by sensor denial. We show that DreamerV3 outperforms other methods in the visual end-to-end navigation task with a dynamic goal - and other methods are not able to learn this. Furthermore, DreamerV3 generally outperforms other methods in sensor-denied environments. In order to improve robustness, we use adversarial training and demonstrate an improved performance in denied environments, although this generally comes with a performance cost on the vanilla environments. We anticipate this benchmark of different DRL methods and the usage of adversarial training to be a starting point for the development of more elaborate navigation strategies that are capable of dealing with uncertain and denied sensor readings.
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