6 citations · 9 across the 2 of their papers we have counts for
6 papers · 1 filter
Maintenance Strategies for Sewer Pipes with Multi-State Degradation and Deep Reinforcement Learning
Lisandro A. Jimenez-Roa, Thiago D. Simão, Zaharah Bukhsh +4
Large-scale infrastructure systems are crucial for societal welfare, and their effective management requires strategic forecasting and intervention methods that account for various…
Robust Active Measuring under Model Uncertainty
Merlijn Krale, Thiago D. Simão, Jana Tumova +1
Partial observability and uncertainty are common problems in sequential decision-making that particularly impede the use of formal models such as Markov decision processes (MDPs).…
Reinforcement Learning by Guided Safe Exploration
Qisong Yang, Thiago D. Simão, Nils Jansen +2
Safety is critical to broadening the application of reinforcement learning (RL). Often, we train RL agents in a controlled environment, such as a laboratory, before deploying them…
More for Less: Safe Policy Improvement With Stronger Performance Guarantees
Patrick Wienhöft, Marnix Suilen, Thiago D. Simão +3
In an offline reinforcement learning setting, the safe policy improvement (SPI) problem aims to improve the performance of a behavior policy according to which sample data has been…
Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation
Yannick Hogewind, Thiago D. Simao, Tal Kachman +1
We address the problem of safe reinforcement learning from pixel observations. Inherent challenges in such settings are (1) a trade-off between reward optimization and adhering to…
Safe Policy Improvement with an Estimated Baseline Policy
Thiago D. Simão, Romain Laroche, Rémi Tachet des Combes
Previous work has shown the unreliability of existing algorithms in the batch Reinforcement Learning setting, and proposed the theoretically-grounded Safe Policy Improvement with B…