3 papers
cs.LG2026
Safe In-Context Reinforcement Learning
Amir Moeini, Minjae Kwon, Alper Kamil Bozkurt +4
In-context reinforcement learning (ICRL) is an emerging RL paradigm where an agent, after pretraining, can adapt to out-of-distribution test tasks without any parameter updates, in…
cs.LG2026
Latent Q-Barrier Shielding for Safe In-Context Reinforcement Learning
Minjae Kwon, Amir Moeini, Shangtong Zhang +1
Safe in-context reinforcement learning (ICRL) adapts online from interaction history without test-time parameter updates while controlling episode cost under a safety budget. Under…
cs.LG2025
Counterfactual Explanations for Continuous Action Reinforcement Learning
Shuyang Dong, Shangtong Zhang, Lu Feng
Reinforcement Learning (RL) has shown great promise in domains like healthcare and robotics but often struggles with adoption due to its lack of interpretability. Counterfactual ex…