RLC 2026
The Causal Reinforcement Learning Workshop
Davide Corsi, Mingxuan Li, Annie Raichev, Sven Koenig, Rina Dechter, Roy Fox
About This Workshop
RL and Causal Inference share a fundamental connection: both aim to model how actions influence outcomes in uncertain environments. Recently, there has been growing interest in bridging these fields to enhance RL algorithms' generalization, robustness, and sample efficiency.
The CausalRL Workshop brings together researchers at the intersection of RL and Causality. Topics include causal offline-to-online RL, causal world modeling, causal discovery in interactive environments, causal representation learning for RL, and causality for robust and safe RL.