6 citations · 23 across the 12 of their papers we have counts for
13 papers
Pessimistic Iterative Planning with RNNs for Robust POMDPs
Maris F. L. Galesloot, Marnix Suilen, Thiago D. Simão +4
Robust POMDPs extend classical POMDPs to incorporate model uncertainty using so-called uncertainty sets on the transition and observation functions, effectively defining ranges of…
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).…
Factored Online Planning in Many-Agent POMDPs
Maris F. L. Galesloot, Thiago D. Simão, Sebastian Junges +1
In centralized multi-agent systems, often modeled as multi-agent partially observable Markov decision processes (MPOMDPs), the action and observation spaces grow exponentially with…
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…
Act-Then-Measure: Reinforcement Learning for Partially Observable Environments with Active Measuring
Merlijn Krale, Thiago D. Simão, Nils Jansen
We study Markov decision processes (MDPs), where agents have direct control over when and how they gather information, as formalized by action-contingent noiselessly observable MDP…
Decision-Making Under Uncertainty: Beyond Probabilities
Thom Badings, Thiago D. Simão, Marnix Suilen +1
This position paper reflects on the state-of-the-art in decision-making under uncertainty. A classical assumption is that probabilities can sufficiently capture all uncertainty in…
Safe Policy Improvement for POMDPs via Finite-State Controllers
Thiago D. Simão, Marnix Suilen, Nils Jansen
We study safe policy improvement (SPI) for partially observable Markov decision processes (POMDPs). SPI is an offline reinforcement learning (RL) problem that assumes access to (1)…
Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model Checking
Dennis Gross, Thiago D. Simao, Nils Jansen +1
Deep Reinforcement Learning (RL) agents are susceptible to adversarial noise in their observations that can mislead their policies and decrease their performance. However, an adver…
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…
Robust Anytime Learning of Markov Decision Processes
Marnix Suilen, Thiago D. Simão, David Parker +1
Markov decision processes (MDPs) are formal models commonly used in sequential decision-making. MDPs capture the stochasticity that may arise, for instance, from imprecise actuator…
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…