Papers
Digital, Intelligence & Platforms
Enhancing RL Generalization with Compositional Causal Components
The paper "Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning" by Xinyue Wang and Biwei Huang introduces a novel framework, WM3C, that enhances reinforcement learning (RL) generalization by decomposing tasks into composable causal components. This approach leverages language to guide the decomposition of the latent space, leading to better generalization in unseen environments.
