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cs.LG2025
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.LG2025
Adaptive Shielding for Safe Reinforcement Learning under Hidden-Parameter Dynamics Shifts
Minjae Kwon, Tyler Ingebrand, Ufuk Topcu +1
Unseen shifts in environment dynamics, driven by hidden parameters such as friction or gravity, create a challenge for maintaining safety. We address this challenge by proposing Ad…