5 papers
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…
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…
Safety Generalization Under Distribution Shift in Safe Reinforcement Learning: A Diabetes Testbed
Minjae Kwon, Josephine Lamp, Lu Feng
Safe Reinforcement Learning (RL) algorithms are typically evaluated under fixed training conditions. We investigate whether training-time safety guarantees transfer to deployment u…
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…
Adaptive Reward Design for Reinforcement Learning
Minjae Kwon, Ingy ElSayed-Aly, Lu Feng
There is a surge of interest in using formal languages such as Linear Temporal Logic (LTL) to precisely and succinctly specify complex tasks and derive reward functions for Reinfor…