2 papers
cs.AI2026
Finite-State Controllers for (Hidden-Model) POMDPs using Deep Reinforcement Learning
David Hudák, Maris F. L. Galesloot, Martin Tappler +3
Solving partially observable Markov decision processes (POMDPs) requires computing policies under imperfect state information. Despite recent advances, the scalability of existing…
cs.CV2025
Does Knowledge About Perceptual Uncertainty Help an Agent in Automated Driving?
Natalie Grabowsky, Annika Mütze, Joshua Wendland +2
Agents in real-world scenarios like automated driving deal with uncertainty in their environment, in particular due to perceptual uncertainty. Although, reinforcement learning is d…