Visual model-based RL methods typically encode image observations into low-dimensional representations in a manner that does not eliminate redundant information. This leaves them susceptible to spurious variations -- changes in task-irrelevant components such as background distractors or lighting conditions. In this paper, we propose a visual model-based RL method that learns a latent representation resilient to such spurious variations. Our training objective encourages the representation to be maximally predictive of dynamics and reward, while constraining the information flow from the observation to the latent representation. We demonstrate that this objective significantly bolsters the resilience of visual model-based RL methods to visual distractors, allowing them to operate in dynamic environments. We then show that while the learned encoder is resilient to spirious variations, it is not invariant under significant distribution shift. To address this, we propose a simple reward-free alignment procedure that enables test time adaptation of the encoder. This allows for quick adaptation to widely differing environments without having to relearn the dynamics and policy. Our effort is a step towards making model-based RL a practical and useful tool for dynamic, diverse domains. We show its effectiveness in simulation benchmarks with significant spurious variations as well as a real-world egocentric navigation task with noisy TVs in the background. Videos and code at https://zchuning.github.io/repo-website/.

翻译：基于视觉的模型强化学习方法通常将图像观测编码为低维表示，但未消除其中的冗余信息。这使得它们容易受到虚假变化的影响，例如背景干扰物或光照条件等任务无关成分的改变。本文提出一种基于视觉的模型强化学习方法，学习对这类虚假变化具有鲁棒性的潜在表示。我们的训练目标鼓励表示最大化地对动力学和奖励具有预测性，同时限制从观测到潜在表示的信息流。实验表明，该目标显著增强了基于视觉的模型强化学习方法对视觉干扰物的鲁棒性，使其能够在动态环境中运行。进一步研究发现，学习到的编码器虽对虚假变化具有鲁棒性，但在显著分布偏移下并非保持不变。为解决此问题，我们提出一种简单的无奖励对齐流程，实现编码器的测试时自适应。这使得编码器能够快速适应差异显著的环境，而无需重新学习动力学和策略。我们的工作是推动基于模型的强化学习成为动态、多样化领域实用工具的重要一步。我们在存在显著虚假变化的模拟基准测试以及背景存在嘈杂电视的真实世界自我中心导航任务中验证了其有效性。视频和代码见 https://zchuning.github.io/repo-website/。