Compiling VGDL into Causal Models
Key takeaway
Reinforcement learning and large language models often struggle to accurately capture the causal mechanics of game environments.
Why it's on PDOOM
PDOOM selected this story for its alignment & control signals: Interpretability.
InterpretabilityFrom ArXiv cs.AI
Reinforcement learning and large language models often struggle to accurately capture the causal mechanics of game environments. Standard reinforcement learning agents tend to rely on spurious correlations, while large language models are prone to hallucinating game rules. Although causal reinforcement learning improves interpretability, there is currently no formal methodology to map complex game mechanics directly into causal models. To address t
By Mohit Jiwatode, Bodo Rosenhahn, Alexander Dockhorn