1 code implementation • 8 May 2024 • Alan A. Lahoud, Erik Schaffernicht, Johannes A. Stork
Learning latent costs of transitions on graphs from trajectories demonstrations under various contextual features is challenging but useful for path planning.
no code implementations • 2 May 2024 • Finn Rietz, Erik Schaffernicht, Stefan Heinrich, Johannes A. Stork
Reinforcement learning policies are typically represented by black-box neural networks, which are non-interpretable and not well-suited for safety-critical domains.
1 code implementation • 3 Oct 2023 • Finn Rietz, Erik Schaffernicht, Stefan Heinrich, Johannes Andreas Stork
PSQD offers the ability to reuse previously learned subtask solutions in a zero-shot composition, followed by an adaptation step.
no code implementations • 20 Sep 2022 • Finn Rietz, Erik Schaffernicht, Todor Stoyanov, Johannes A. Stork
Combining learned policies in a prioritized, ordered manner is desirable because it allows for modular design and facilitates data reuse through knowledge transfer.