Imagen de Portada

Learning The Minimum Action Distance

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Steccanella, Lorenzo, Evans, Joshua B., Şimşek, Özgür, Jonsson, Anders
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Creating Multi-Level Skill Hierarchies in Reinforcement Learning
por: Evans, Joshua B., et al.
Publicado: (2023)

Causal Discovery in Action: Learning Chain-Reaction Mechanisms from Interventions
por: Panayiotou, Panayiotis, et al.
Publicado: (2026)

Curricula for Learning Robust Policies with Factored State Representations in Changing Environments
por: Panayiotou, Panayiotis, et al.
Publicado: (2024)

The Terminal Representation in Reinforcement Learning
por: Esterhuysen, Amir, et al.
Publicado: (2026)

Accelerating Task Generalisation with Multi-Level Skill Hierarchies
por: Cannon, Thomas P, et al.
Publicado: (2024)

Hierarchical Orchestra of Policies
por: Cannon, Thomas P, et al.
Publicado: (2024)

Globally Optimal Hierarchical Reinforcement Learning for Linearly-Solvable Markov Decision Processes
por: Infante, Guillermo, et al.
Publicado: (2021)

Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamics
por: Amaya-Corredor, Santiago, et al.
Publicado: (2026)

CausalProfiler: Generating Synthetic Benchmarks for Rigorous and Transparent Evaluation of Causal Machine Learning
por: Panayiotou, Panayiotis, et al.
Publicado: (2025)

Hierarchical Average-Reward Linearly-solvable Markov Decision Processes
por: Infante, Guillermo, et al.
Publicado: (2024)

Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption
por: Poinsot, Audrey, et al.
Publicado: (2025)

Planning with a Learned Policy Basis to Optimally Solve Complex Tasks
por: Infante, Guillermo, et al.
Publicado: (2024)

Learning Associative Memories with Gradient Descent
por: Cabannes, Vivien, et al.
Publicado: (2024)

Breaking the Grid: Distance-Guided Reinforcement Learning in Large Discrete Action Spaces
por: Hoppe, Heiko, et al.
Publicado: (2026)

Federated Learning-Based Risk-Aware Decision toMitigate Fake Task Impacts on CrowdsensingPlatforms
por: Chen, Zhiyan, et al.
Publicado: (2021)

Provably Efficient Exploration in Reward Machines with Low Regret
por: Bourel, Hippolyte, et al.
Publicado: (2024)

Multiclass Local Calibration with the Jensen-Shannon Distance
por: Barbera, Cesare, et al.
Publicado: (2025)

Tractable Offline Learning of Regular Decision Processes
por: Deb, Ahana, et al.
Publicado: (2024)

CUER: Corrected Uniform Experience Replay for Off-Policy Continuous Deep Reinforcement Learning Algorithms
por: Yenicesu, Arda Sarp, et al.
Publicado: (2024)

Understanding Federated Learning from IID to Non-IID dataset: An Experimental Study
por: Seo, Jungwon, et al.
Publicado: (2025)

Is Distance Matrix Enough for Geometric Deep Learning?
por: Li, Zian, et al.
Publicado: (2023)

Optimal Policy Minimum Bayesian Risk
por: Astudillo, Ramón Fernandez, et al.
Publicado: (2025)

Learning to Act without Actions
por: Schmidt, Dominik, et al.
Publicado: (2023)

Learning Iterative Reasoning through Energy Diffusion
por: Du, Yilun, et al.
Publicado: (2024)

Learn-to-Distance: Distance Learning for Detecting LLM-Generated Text
por: Zhou, Hongyi, et al.
Publicado: (2026)

GEDAN: Learning the Edit Costs for Graph Edit Distance
por: Leonardi, Francesco, et al.
Publicado: (2025)

Auxiliary Reward Generation with Transition Distance Representation Learning
por: Li, Siyuan, et al.
Publicado: (2024)

Distance-Based Tree-Sliced Wasserstein Distance
por: Tran, Hoang V., et al.
Publicado: (2025)

Learning to Generate All Feasible Actions
por: Theile, Mirco, et al.
Publicado: (2023)

Action-Adaptive Continual Learning: Enabling Policy Generalization under Dynamic Action Spaces
por: Pan, Chaofan, et al.
Publicado: (2025)

Local Pairwise Distance Matching for Backpropagation-Free Reinforcement Learning
por: Tanneberg, Daniel
Publicado: (2025)

Learning Unified Distance Metric for Heterogeneous Attribute Data Clustering
por: Zhang, Yiqun, et al.
Publicado: (2026)

DAWM: Diffusion Action World Models for Offline Reinforcement Learning via Action-Inferred Transitions
por: Li, Zongyue, et al.
Publicado: (2025)

Counterfactual Explanations for Continuous Action Reinforcement Learning
por: Dong, Shuyang, et al.
Publicado: (2025)

Learning Safe Numeric Planning Action Models
por: Mordoch, Argaman, et al.
Publicado: (2023)

In-Context Reinforcement Learning for Variable Action Spaces
por: Sinii, Viacheslav, et al.
Publicado: (2023)

Learning Action-based Representations Using Invariance
por: Rudolph, Max, et al.
Publicado: (2024)

Learning Action Embeddings for Off-Policy Evaluation
por: Cief, Matej, et al.
Publicado: (2023)

Approximating Shapley Explanations in Reinforcement Learning
por: Beechey, Daniel, et al.
Publicado: (2025)

Using Kolmogorov-Smirnov Distance for Measuring Distribution Shift in Machine Learning
por: Tonguz, Ozan K., et al.
Publicado: (2025)