Image de couverture de livre

Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Kohler, Hector, Delfosse, Quentin, Akrour, Riad, Kersting, Kristian, Preux, Philippe
Format:	Preprint
Publié:	2024
Sujets:	Artificial Intelligence Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Evaluating Interpretable Reinforcement Learning by Distilling Policies into Programs
par: Kohler, Hector, et autres
Publié: (2025)

Limits of Actor-Critic Algorithms for Decision Tree Policies Learning in IBMDPs
par: Kohler, Hector, et autres
Publié: (2023)

Towards a Research Community in Interpretable Reinforcement Learning: the InterpPol Workshop
par: Kohler, Hector, et autres
Publié: (2024)

Breiman meets Bellman: Non-Greedy Decision Trees with MDPs
par: Kohler, Hector, et autres
Publié: (2023)

When (and How) to Trust the Expert: Diagnosing Query-Time Expert-Guided Reinforcement Learning
par: Berthelot, Yann, et autres
Publié: (2026)

HackAtari: Atari Learning Environments for Robust and Continual Reinforcement Learning
par: Delfosse, Quentin, et autres
Publié: (2024)

GRAIL: Autonomous Concept Grounding for Neuro-Symbolic Reinforcement Learning
par: Shindo, Hikaru, et autres
Publié: (2026)

BlendRL: A Framework for Merging Symbolic and Neural Policy Learning
par: Shindo, Hikaru, et autres
Publié: (2024)

Interpretable end-to-end Neurosymbolic Reinforcement Learning agents
par: Grandien, Nils, et autres
Publié: (2024)

PB$^2$: Preference Space Exploration via Population-Based Methods in Preference-Based Reinforcement Learning
par: Driss, Brahim, et autres
Publié: (2025)

Deep Reinforcement Learning via Object-Centric Attention
par: Blüml, Jannis, et autres
Publié: (2025)

OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments
par: Delfosse, Quentin, et autres
Publié: (2023)

StaQ it! Growing neural networks for Policy Mirror Descent
par: Shilova, Alena, et autres
Publié: (2025)

Boosting deep Reinforcement Learning using pretraining with Logical Options
par: Ye, Zihan, et autres
Publié: (2026)

Deep Reinforcement Learning Agents are not even close to Human Intelligence
par: Delfosse, Quentin, et autres
Publié: (2025)

Pix2Code: Learning to Compose Neural Visual Concepts as Programs
par: Wüst, Antonia, et autres
Publié: (2024)

Better Decisions through the Right Causal World Model
par: Dillies, Elisabeth, et autres
Publié: (2025)

Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents
par: Delfosse, Quentin, et autres
Publié: (2024)

Scheduling That Speaks: An Interpretable Programmatic Reinforcement Learning Framework
par: Hu, Chengpeng, et autres
Publié: (2026)

IDEQ: an improved diffusion model for the TSP
par: Basson, Mickael, et autres
Publié: (2024)

LLMs Gaming Verifiers: RLVR can Lead to Reward Hacking
par: Helff, Lukas, et autres
Publié: (2026)

Reclaiming the Source of Programmatic Policies: Programmatic versus Latent Spaces
par: Carvalho, Tales H., et autres
Publié: (2024)

Adaptive Rational Activations to Boost Deep Reinforcement Learning
par: Delfosse, Quentin, et autres
Publié: (2021)

Synthesizing Programmatic Reinforcement Learning Policies with Large Language Model Guided Search
par: Liu, Max, et autres
Publié: (2024)

Learning to Intervene on Concept Bottlenecks
par: Steinmann, David, et autres
Publié: (2023)

Neural DNF-MT: A Neuro-symbolic Approach for Learning Interpretable and Editable Policies
par: Baugh, Kexin Gu, et autres
Publié: (2025)

Deep Classifier Mimicry without Data Access
par: Braun, Steven, et autres
Publié: (2023)

DiPRL: Learning Discrete Programmatic Policies via Architecture Entropy Regularization
par: Hu, Chengpeng, et autres
Publié: (2026)

Searching for Programmatic Policies in Semantic Spaces
par: Moraes, Rubens O., et autres
Publié: (2024)

EXPIL: Explanatory Predicate Invention for Learning in Games
par: Sha, Jingyuan, et autres
Publié: (2024)

Towards Interpretable Reinforcement Learning with Constrained Normalizing Flow Policies
par: Rietz, Finn, et autres
Publié: (2024)

Object Centric Concept Bottlenecks
par: Steinmann, David, et autres
Publié: (2025)

Hyperparameter Optimization via Interacting with Probabilistic Circuits
par: Seng, Jonas, et autres
Publié: (2025)

Learning by Self-Explaining
par: Stammer, Wolfgang, et autres
Publié: (2023)

PolicyEvolve: Evolving Programmatic Policies by LLMs for multi-player games via Population-Based Training
par: Lv, Mingrui, et autres
Publié: (2025)

Tractable Representation Learning with Probabilistic Circuits
par: Braun, Steven, et autres
Publié: (2025)

Three Pathways to Neurosymbolic Reinforcement Learning with Interpretable Model and Policy Networks
par: Graf, Peter, et autres
Publié: (2024)

Prism: Policy Reuse via Interpretable Strategy Mapping in Reinforcement Learning
par: Pravetz, Thomas
Publié: (2026)

Augmented Bayesian Policy Search
par: Kallel, Mahdi, et autres
Publié: (2024)

Exploring the Adversarial Capabilities of Large Language Models
par: Struppek, Lukas, et autres
Publié: (2024)