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cs.LG2026
Finite-Time Guarantees for Constrained Reinforcement Learning under Partial Observability
Valentina Zaccaria, Philip Jordan, Tingting Ni +2
We study reinforcement learning in constrained partially observable Markov decision processes (CPOMDPs), where an agent maximizes reward subject to a cost constraint using only inc…
cs.LG2026
Provably Safe Sim-to-Real Transfer
Tingting Ni, Maryam Kamgarpour
We address safe sim-to-real transfer, in which an agent leverages an imperfect simulator and limited real-world interaction while ensuring safety throughout data collection in the…
cs.LG2026
Constrained Meta Reinforcement Learning with Provable Test-Time Safety
Tingting Ni, Maryam Kamgarpour
Meta reinforcement learning (RL) allows agents to leverage experience across a distribution of tasks on which the agent can train at will, enabling faster learning of optimal polic…