This episode delves into Neurosymbolic Reinforcement Learning and the SCoBots (Successive Concept Bottlenecks Agents) framework, designed to make AI agents more interpretable and trustworthy. SCoBots break down reinforcement learning tasks into interpretable steps based on object-centric relational concepts, combining neural networks with symbolic AI.Key components include the Object Extractor (identifies objects from images), Relation Extractor (derives relational concepts like speed and distance), and Action Selector (chooses actions using interpretable rule sets). The episode highlights research on Atari games, demonstrating SCoBots' effectiveness while maintaining transparency. Future research aims to improve object extraction, rule interpretability, and extend the framework to more complex environments, providing a powerful yet transparent approach to AI.

https://arxiv.org/pdf/2410.14371