Search Results for author:
Found 3 papers, 1 papers with code
Towards Interpretable Reinforcement Learning with Constrained Normalizing Flow Policies
Reinforcement learning policies are typically represented by black-box neural networks, which are non-interpretable and not well-suited for safety-critical domains.
Prioritized Soft Q-Decomposition for Lexicographic Reinforcement Learning
PSQD offers the ability to reuse previously learned subtask solutions in a zero-shot composition, followed by an adaptation step.
Towards Task-Prioritized Policy Composition
Combining learned policies in a prioritized, ordered manner is desirable because it allows for modular design and facilitates data reuse through knowledge transfer.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
