6 citations · 23 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
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…
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)…
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…