Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Reizinger, Patrik, Mucsányi, Bálint, Guo, Siyuan, Eysenbach, Benjamin, Schölkopf, Bernhard, Brendel, Wieland |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning
by: Reizinger, Patrik, et al.
Published: (2024)
by: Reizinger, Patrik, et al.
Published: (2024)
Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research
by: Reizinger, Patrik, et al.
Published: (2025)
by: Reizinger, Patrik, et al.
Published: (2025)
Estimating Treatment Effects with Independent Component Analysis
by: Reizinger, Patrik, et al.
Published: (2025)
by: Reizinger, Patrik, et al.
Published: (2025)
Who Guards the Guardians? The Challenges of Evaluating Identifiability of Learned Representations
by: Joshi, Shruti, et al.
Published: (2026)
by: Joshi, Shruti, et al.
Published: (2026)
Cross-Entropy Is All You Need To Invert the Data Generating Process
by: Reizinger, Patrik, et al.
Published: (2024)
by: Reizinger, Patrik, et al.
Published: (2024)
UpSkill: Mutual Information Skill Learning for Structured Response Diversity in LLMs
by: Shah, Devan, et al.
Published: (2026)
by: Shah, Devan, et al.
Published: (2026)
An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis
by: Rajendran, Goutham, et al.
Published: (2023)
by: Rajendran, Goutham, et al.
Published: (2023)
Horizon Generalization in Reinforcement Learning
by: Myers, Vivek, et al.
Published: (2025)
by: Myers, Vivek, et al.
Published: (2025)
Learning Nonlinear Causal Reductions to Explain Reinforcement Learning Policies
by: Kekić, Armin, et al.
Published: (2025)
by: Kekić, Armin, et al.
Published: (2025)
Out-of-Variable Generalization for Discriminative Models
by: Guo, Siyuan, et al.
Published: (2023)
by: Guo, Siyuan, et al.
Published: (2023)
Unifying Goal-Conditioned RL and Unsupervised Skill Learning via Control-Maximization
by: Modirshanechi, Alireza, et al.
Published: (2026)
by: Modirshanechi, Alireza, et al.
Published: (2026)
Can a MISL Fly? Analysis and Ingredients for Mutual Information Skill Learning
by: Zheng, Chongyi, et al.
Published: (2024)
by: Zheng, Chongyi, et al.
Published: (2024)
Preference Elicitation for Offline Reinforcement Learning
by: Pace, Alizée, et al.
Published: (2024)
by: Pace, Alizée, et al.
Published: (2024)
Can We Really Learn One Representation to Optimize All Rewards?
by: Zheng, Chongyi, et al.
Published: (2026)
by: Zheng, Chongyi, et al.
Published: (2026)
Temporal Representations for Exploration: Learning Complex Exploratory Behavior without Extrinsic Rewards
by: Mohamed, Faisal, et al.
Published: (2026)
by: Mohamed, Faisal, et al.
Published: (2026)
InfoNCE: Identifying the Gap Between Theory and Practice
by: Rusak, Evgenia, et al.
Published: (2024)
by: Rusak, Evgenia, et al.
Published: (2024)
Identifying Intervenable and Interpretable Features via Orthogonality Regularization
by: Miller, Moritz, et al.
Published: (2026)
by: Miller, Moritz, et al.
Published: (2026)
Behavior-Consistent Deep Reinforcement Learning
by: Hussing, Marcel, et al.
Published: (2026)
by: Hussing, Marcel, et al.
Published: (2026)
Dynamics Reveals Structure: Challenging the Linear Propagation Assumption
by: Chang, Hoyeon, et al.
Published: (2026)
by: Chang, Hoyeon, et al.
Published: (2026)
A Single Goal is All You Need: Skills and Exploration Emerge from Contrastive RL without Rewards, Demonstrations, or Subgoals
by: Liu, Grace, et al.
Published: (2024)
by: Liu, Grace, et al.
Published: (2024)
Rule Extrapolation in Language Models: A Study of Compositional Generalization on OOD Prompts
by: Mészáros, Anna, et al.
Published: (2024)
by: Mészáros, Anna, et al.
Published: (2024)
Counterfactual reasoning: an analysis of in-context emergence
by: Miller, Moritz, et al.
Published: (2025)
by: Miller, Moritz, et al.
Published: (2025)
Improving Large Language Model Safety with Contrastive Representation Learning
by: Simko, Samuel, et al.
Published: (2025)
by: Simko, Samuel, et al.
Published: (2025)
A Probabilistic Model Behind Self-Supervised Learning
by: Bizeul, Alice, et al.
Published: (2024)
by: Bizeul, Alice, et al.
Published: (2024)
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models
by: Rajendran, Goutham, et al.
Published: (2024)
by: Rajendran, Goutham, et al.
Published: (2024)
Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs
by: Guo, Siyuan, et al.
Published: (2024)
by: Guo, Siyuan, et al.
Published: (2024)
From Isolation to Entanglement: When Do Interpretability Methods Identify and Disentangle Known Concepts?
by: Mueller, Aaron, et al.
Published: (2025)
by: Mueller, Aaron, et al.
Published: (2025)
Is Temporal Difference Learning the Gold Standard for Stitching in RL?
by: Bortkiewicz, Michał, et al.
Published: (2025)
by: Bortkiewicz, Michał, et al.
Published: (2025)
Position: Understanding LLMs Requires More Than Statistical Generalization
by: Reizinger, Patrik, et al.
Published: (2024)
by: Reizinger, Patrik, et al.
Published: (2024)
Causality is Key for Interpretability Claims to Generalise
by: Joshi, Shruti, et al.
Published: (2026)
by: Joshi, Shruti, et al.
Published: (2026)
MATH-Beyond: A Benchmark for RL to Expand Beyond the Base Model
by: Mayilvahanan, Prasanna, et al.
Published: (2025)
by: Mayilvahanan, Prasanna, et al.
Published: (2025)
Contrastive Representations for Temporal Reasoning
by: Ziarko, Alicja, et al.
Published: (2025)
by: Ziarko, Alicja, et al.
Published: (2025)
SkillTree: Explainable Skill-Based Deep Reinforcement Learning for Long-Horizon Control Tasks
by: Wen, Yongyan, et al.
Published: (2024)
by: Wen, Yongyan, et al.
Published: (2024)
Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations
by: Liang, Yongyuan, et al.
Published: (2023)
by: Liang, Yongyuan, et al.
Published: (2023)
Skill-based Safe Reinforcement Learning with Risk Planning
by: Zhang, Hanping, et al.
Published: (2025)
by: Zhang, Hanping, et al.
Published: (2025)
Skill-R1: Agent Skill Evolution via Reinforcement Learning
by: Vishe, Yash, et al.
Published: (2026)
by: Vishe, Yash, et al.
Published: (2026)
laplax -- Laplace Approximations with JAX
by: Weber, Tobias, et al.
Published: (2025)
by: Weber, Tobias, et al.
Published: (2025)
VendiRL: A Framework for Self-Supervised Reinforcement Learning of Diversely Diverse Skills
by: Lintunen, Erik M.
Published: (2025)
by: Lintunen, Erik M.
Published: (2025)
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning
by: Batra, Sumeet, et al.
Published: (2023)
by: Batra, Sumeet, et al.
Published: (2023)
Skill-Critic: Refining Learned Skills for Hierarchical Reinforcement Learning
by: Hao, Ce, et al.
Published: (2023)
by: Hao, Ce, et al.
Published: (2023)
Similar Items
-
Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning
by: Reizinger, Patrik, et al.
Published: (2024) -
Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research
by: Reizinger, Patrik, et al.
Published: (2025) -
Estimating Treatment Effects with Independent Component Analysis
by: Reizinger, Patrik, et al.
Published: (2025) -
Who Guards the Guardians? The Challenges of Evaluating Identifiability of Learned Representations
by: Joshi, Shruti, et al.
Published: (2026) -
Cross-Entropy Is All You Need To Invert the Data Generating Process
by: Reizinger, Patrik, et al.
Published: (2024)