Hugging Face Trending Papers
Jul 28
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.
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.
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arXiv Machine Learning
Jun 4
Explainably Safe Reinforcement Learning
arXiv:2606. 04634v1 Announce Type: new Abstract: Trust in a decision-making system requires both safety guarantees and the ability to interpret and understand its behavior.
arXiv AI
Aug 10
Interpretable reinforcement learning with decision-tree pruning
arXiv:2608. 07151v1 Announce Type: cross Abstract: Reinforcement learning policies are difficult to inspect, but interpreting them is a prerequisite for trustworthiness.
arXiv Machine Learning
Aug 3
Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions
arXiv:2307. 10524v3 Announce Type: replace Abstract: We study the tradeoff between consistency and robustness in the context of a single-trajectory time-varying Markov Decision Process (MDP) with untrusted machine-learned advice.
Hugging Face Trending Papers
Aug 3
Optimizing Minimax Regret in Uncertain MDPs with Small Sets of Policies
Sequential decision-making in real-world applications often involves uncertainty about the environment's model. Uncertain Markov decision processes (UMDPs) represent the possible environments as a set of MDPs with shared states and actions but potentially different transition probabilities and rewards.
arXiv AI
Jun 10
Bellman-Taylor Score Decoding for Markov Decision Processes with State-Dependent Feasible Action Sets
arXiv:2606. 10979v1 Announce Type: new Abstract: Many Markov decision processes (MDPs) in operations research have feasible actions that are state dependent and defined implicitly by various operational constraints.
arXiv Machine Learning
Aug 4
Optimizing Minimax Regret in Uncertain MDPs with Small Sets of Policies
arXiv:2608. 02509v1 Announce Type: cross Abstract: Sequential decision-making in real-world applications often involves uncertainty about the environment's model.
arXiv Machine Learning
Aug 10
Learning Suffers More Than the Policy Class Under Partial Observability: A Closed-Form Analysis
arXiv:2608. 07228v1 Announce Type: new Abstract: When a reinforcement learning agent cannot observe the full state, we usually blame its policies: it cannot see enough to represent a good one.
arXiv Machine Learning
Aug 11
Finite Constant Frontiers and Auditable Regret Certificates for Average-Reward Reinforcement Learning
arXiv:2608. 07725v1 Announce Type: new Abstract: Average-reward reinforcement-learning regret is known up to logarithmic factors, but the numerical content of published guarantees is difficult to compare because probability mode, structural parameter, logarithmic normalization, prior information, and planning assumptions differ.
arXiv Machine Learning
Jul 1
End-to-End Efficient RL for Linear Bellman Complete MDPs with Deterministic Transitions
arXiv:2603. 23461v2 Announce Type: replace Abstract: We study reinforcement learning (RL) with linear function approximation in Markov Decision Processes (MDPs) satisfying \emph{linear Bellman completeness} -- a fundamental setting where the Bellman backup of any linear value function remains linear.
arXiv Machine Learning
Jun 2
Online Learning in MDPs with Partially Adversarial Transitions and Losses
arXiv:2602. 09474v2 Announce Type: replace Abstract: We study reinforcement learning in MDPs whose transition function is stochastic at most steps but may behave adversarially at a fixed subset of $\Lambda$ steps per episode.
arXiv AI
Jul 20
From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems
arXiv:2607. 15459v1 Announce Type: new Abstract: A trained deep reinforcement learning policy is a black box, and we ask whether it can be made explainable by rewriting it as an executable logic program that reproduces its behaviour and that a person can read, a logic engine can run, and an optimizer can edit.