Search Results for author: Edi Muskardin

Found 1 papers, 0 papers with code

Reinforcement Learning under Partial Observability Guided by Learned Environment Models

no code implementations23 Jun 2022 Edi Muskardin, Martin Tappler, Bernhard K. Aichernig, Ingo Pill

In practical applications, we can rarely assume full observability of a system's environment, despite such knowledge being important for determining a reactive control system's precise interaction with its environment.

Q-Learning reinforcement-learning +1

Cannot find the paper you are looking for? You can Submit a new open access paper.