ERC Starting Grant · 2022
Data-Driven Verification and Learning Under Uncertainty
Reinforcement learning (RL) agents learn to behave optimally via trial and error, without the need to encode complicated behavior explicitly. However, RL generally lacks mechanisms to constantly ensure correct behavior regarding sophisticated task and safety specifications. Formal verification (FV), and in particular model checking, provides formal guarantees on a system's correctness based on rigorous methods and precise specifications. Despite active development by researchers from all over the world, fundamental challenges obstruct the application of FV to RL so far. We identify three key challenges that frame the objectives of this proposal. (1) Complex environments with large degrees…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.