Cover Image

Breaking Habits: On the Role of the Advantage Function in Learning Causal State Representations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Suau, Miguel
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Dynamically Scaled Activation Steering
by: Ferrando, Alex, et al.
Published: (2025)

Causal Structure and Representation Learning with Biomedical Applications
by: Uhler, Caroline, et al.
Published: (2025)

Causal Neighbourhood Learning for Invariant Graph Representations
by: Job, Simi, et al.
Published: (2026)

Disentangled Representation Learning for Causal Inference with Instruments
by: Cheng, Debo, et al.
Published: (2024)

Deep Causal Learning: Representation, Discovery and Inference
by: Deng, Zizhen, et al.
Published: (2022)

Smooth Gate Functions for Soft Advantage Policy Optimization
by: Denisov, Egor, et al.
Published: (2026)

Toward Temporal Causal Representation Learning with Tensor Decomposition
by: Chen, Jianhong, et al.
Published: (2025)

Humanoid-inspired Causal Representation Learning for Domain Generalization
by: Tao, Ze, et al.
Published: (2025)

Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning
by: Reizinger, Patrik, et al.
Published: (2024)

Multi-View Causal Representation Learning with Partial Observability
by: Yao, Dingling, et al.
Published: (2023)

Causal Debiasing Medical Multimodal Representation Learning with Missing Modalities
by: Zhu, Xiaoguang, et al.
Published: (2025)

A Sparsity Principle for Partially Observable Causal Representation Learning
by: Xu, Danru, et al.
Published: (2024)

Seeking the Sufficiency and Necessity Causal Features in Multimodal Representation Learning
by: Chen, Boyu, et al.
Published: (2024)

Measure-Theoretic Anti-Causal Representation Learning
by: Behnam, Arman, et al.
Published: (2025)

CARL: Causality-guided Architecture Representation Learning for an Interpretable Performance Predictor
by: Ji, Han, et al.
Published: (2025)

Towards Robust Trajectory Representations: Isolating Environmental Confounders with Causal Learning
by: Luo, Kang, et al.
Published: (2024)

Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment
by: von Kügelgen, Julius
Published: (2024)

A Closer Look at the Application of Causal Inference in Graph Representation Learning
by: Gao, Hang, et al.
Published: (2026)

Towards the Reusability and Compositionality of Causal Representations
by: Talon, Davide, et al.
Published: (2024)

On the Representation of Pairwise Causal Background Knowledge and Its Applications in Causal Inference
by: Fang, Zhuangyan, et al.
Published: (2022)

Learning Causally Invariant Reward Functions from Diverse Demonstrations
by: Ovinnikov, Ivan, et al.
Published: (2024)

Advantage-Guided Diffusion for Model-Based Reinforcement Learning
by: Foffano, Daniele, et al.
Published: (2026)

FedSDWC: Federated Synergistic Dual-Representation Weak Causal Learning for OOD
by: Huang, Zhenyuan, et al.
Published: (2025)

CaberNet: Causal Representation Learning for Cross-Domain HVAC Energy Prediction
by: Zhai, Kaiyuan, et al.
Published: (2025)

Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs
by: Ding, Pengfei, et al.
Published: (2024)

BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning
by: Lin, Haohong, et al.
Published: (2024)

Causal State Distillation for Explainable Reinforcement Learning
by: Lu, Wenhao, et al.
Published: (2023)

HyperTransport: Amortized Conditioning of T2I Generative Models
by: Maiorca, Valentino, et al.
Published: (2026)

A Survey of State Representation Learning for Deep Reinforcement Learning
by: Echchahed, Ayoub, et al.
Published: (2025)

Memory-Based Advantage Shaping for LLM-Guided Reinforcement Learning
by: Nourzad, Narjes, et al.
Published: (2026)

Towards Interpretable Deep Generative Models via Causal Representation Learning
by: Moran, Gemma E., et al.
Published: (2025)

Identifying General Mechanism Shifts in Linear Causal Representations
by: Chen, Tianyu, et al.
Published: (2024)

From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling
by: Komanduri, Aneesh, et al.
Published: (2023)

ADORA: Training Reasoning Models with Dynamic Advantage Estimation on Reinforcement Learning
by: Ren, Qingnan, et al.
Published: (2026)

An Advantage-based Optimization Method for Reinforcement Learning in Large Action Space
by: Lin, Hai, et al.
Published: (2024)

Stable Offline Value Function Learning with Bisimulation-based Representations
by: Pavse, Brahma S., et al.
Published: (2024)

Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models
by: Rajendran, Goutham, et al.
Published: (2024)

Learning Efficiency Meets Symmetry Breaking
by: Bai, Yingbin, et al.
Published: (2025)

Bad Habits: Policy Confounding and Out-of-Trajectory Generalization in RL
by: Suau, Miguel, et al.
Published: (2023)

Sanity Checking Causal Representation Learning on a Simple Real-World System
by: Gamella, Juan L., et al.
Published: (2025)