1 citations · 1 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
Convergence of a model-free entropy-regularized inverse reinforcement learning algorithm
Titouan Renard, Andreas Schlaginhaufen, Tingting Ni +1
Given a dataset of expert demonstrations, inverse reinforcement learning (IRL) aims to recover a reward for which the expert is optimal. This work proposes a model-free algorithm t…
A safe exploration approach to constrained Markov decision processes
Tingting Ni, Maryam Kamgarpour
We consider discounted infinite-horizon constrained Markov decision processes (CMDPs), where the goal is to find an optimal policy that maximizes the expected cumulative reward whi…