The field of vision-based reinforcement learning is moving towards developing more robust and generalizable methods for state estimation and control. Recent research focuses on integrating spatial and temporal features to improve state representation and policy learning. One of the key challenges addressed is the presence of visual distractions, such as shadows, clouds, and light, which can negatively impact policy performance. To overcome this, researchers are exploring novel architectures and techniques, including self-predictive dynamics, dual contrastive learning, and modular recurrence. These advancements have shown promising results in improving the robustness and generalization of vision-based reinforcement learning methods. Notable papers include:

• A study introducing a novel neural architecture that integrates spatial feature extraction and temporal modeling for effective state representation.
• A method using self-predictive dynamics to extract task-relevant features efficiently, even in unseen observations after training.
• A zero-shot model-based reinforcement learning approach that enhances the robustness of the world model against visual distractions.
• A framework for continual learning with dynamic capabilities, enabling policy generalization under dynamic action spaces.
• A modular recurrent architecture for universal morphology control, improving generalization to new, unseen robots.