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Found 4 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.
Diversity for Contingency: Learning Diverse Behaviors for Efficient Adaptation and Transfer
Discovering all useful solutions for a given task is crucial for transferable RL agents, to account for changes in the task or transition dynamics.
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.
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