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ORCIDThiago D. Simão
Person information
- affiliation: Radboud University, Nijmegen, The Netherlands
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2020 – today
- 2023
[b1]Thiago D. Simão
:
Safe Online and Offline Reinforcement Learning. Delft University of Technology, Netherlands, 2023- [j1]Qisong Yang, Thiago D. Simão, Simon H. Tindemans, Matthijs T. J. Spaan:
Safety-constrained reinforcement learning with a distributional safety critic. Mach. Learn. 112(3): 859-887 (2023) - [c10]Dennis Gross, Thiago D. Simão, Nils Jansen, Guillermo A. Pérez:
Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model Checking. ICAART (3) 2023: 501-508 - [i8]Thiago D. Simão, Marnix Suilen, Nils Jansen:
Safe Policy Improvement for POMDPs via Finite-State Controllers. CoRR abs/2301.04939 (2023) - [i7]Thom S. Badings, Thiago D. Simão, Marnix Suilen, Nils Jansen:
Decision-Making Under Uncertainty: Beyond Probabilities. CoRR abs/2303.05848 (2023) - [i6]Merlijn Krale, Thiago D. Simão, Nils Jansen:
Act-Then-Measure: Reinforcement Learning for Partially Observable Environments with Active Measuring. CoRR abs/2303.08271 (2023) - [i5]Patrick Wienhöft, Marnix Suilen, Thiago D. Simão, Clemens Dubslaff, Christel Baier, Nils Jansen:
More for Less: Safe Policy Improvement With Stronger Performance Guarantees. CoRR abs/2305.07958 (2023) - 2022
- [c9]Danial Kamran, Thiago D. Simão, Qisong Yang, Canmanie T. Ponnambalam, Johannes Fischer, Matthijs T. J. Spaan, Martin Lauer:
A Modern Perspective on Safe Automated Driving for Different Traffic Dynamics Using Constrained Reinforcement Learning. ITSC 2022: 4017-4023 - [c8]Marnix Suilen, Thiago D. Simão, David Parker, Nils Jansen:
Robust Anytime Learning of Markov Decision Processes. NeurIPS 2022 - [i4]Marnix Suilen, Thiago D. Simão, Nils Jansen, David Parker:
Robust Anytime Learning of Markov Decision Processes. CoRR abs/2205.15827 (2022) - [i3]Yannick Hogewind, Thiago D. Simão, Tal Kachman, Nils Jansen:
Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation. CoRR abs/2210.01801 (2022) - [i2]Dennis Gross, Thiago D. Simão, Nils Jansen, Guillermo A. Pérez:
Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model Checking. CoRR abs/2212.05337 (2022) - 2021
- [c7]Qisong Yang, Thiago D. Simão, Simon H. Tindemans, Matthijs T. J. Spaan:
WCSAC: Worst-Case Soft Actor Critic for Safety-Constrained Reinforcement Learning. AAAI 2021: 10639-10646 - [c6]Thiago D. Simão, Nils Jansen, Matthijs T. J. Spaan:
AlwaysSafe: Reinforcement Learning without Safety Constraint Violations during Training. AAMAS 2021: 1226-1235 - 2020
- [c5]Thiago D. Simão, Romain Laroche, Rémi Tachet des Combes:
Safe Policy Improvement with an Estimated Baseline Policy. AAMAS 2020: 1269-1277
2010 – 2019
- 2019
- [c4]Thiago D. Simão, Matthijs T. J. Spaan:
Safe Policy Improvement with Baseline Bootstrapping in Factored Environments. AAAI 2019: 4967-4974 - [c3]Thiago D. Simão, Matthijs T. J. Spaan:
Structure Learning for Safe Policy Improvement. IJCAI 2019: 3453-3459 - [c2]Thiago D. Simão:
Safe and Sample-Efficient Reinforcement Learning Algorithms for Factored Environments. IJCAI 2019: 6460-6461 - [i1]Thiago D. Simão, Romain Laroche, Rémi Tachet des Combes:
Safe Policy Improvement with an Estimated Baseline Policy. CoRR abs/1909.05236 (2019) - 2018
- [c1]Ignasi Andrés, Leliane Nunes de Barros, Denis Deratani Mauá, Thiago D. Simão:
When a Robot Reaches Out for Human Help. IBERAMIA 2018: 277-289
Coauthor Index
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