arXiv AI
Jul 29
dtControl2+$\varepsilon$: Trading Optimality for Explainability in MDPs via Decision Trees
arXiv:2607. 25925v1 Announce Type: new Abstract: Over the past decade, decision trees have been used to represent controllers (a.
dtControl2+$\varepsilon$: Trading Optimality for Explainability in MDPs via Decision Trees
Over the past decade, decision trees have been used to represent controllers (a. k.
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Explainably Safe Reinforcement Learning
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Bellman-Taylor Score Decoding for Markov Decision Processes with State-Dependent Feasible Action Sets
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arXiv AI
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SPOTting the Future: Lookahead Explanations for Deep Reinforcement Learning
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Finite Constant Frontiers and Auditable Regret Certificates for Average-Reward Reinforcement Learning
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Optimizing Minimax Regret in Uncertain MDPs with Small Sets of Policies
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arXiv Machine Learning
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End-to-End Efficient RL for Linear Bellman Complete MDPs with Deterministic Transitions
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Learning Policy from a Single Trajectory in Average-Reward Markov Decision Process
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arXiv AI
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From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems
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