Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning | Paper Citation Record

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This paper cites Variational Option Discovery Algorithms.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Variational Option Discovery Algorithms

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This paper cites A Probabilistic Model Behind Self-Supervised Learning.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning A Probabilistic Model Behind Self-Supervised Learning

Reference 2

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This paper cites Large-Scale Study of Curiosity-Driven Learning.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Large-Scale Study of Curiosity-Driven Learning

Reference 3

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This paper cites A Simple Framework for Contrastive Learning of Visual Representations.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning A Simple Framework for Contrastive Learning of Visual Representations

Reference 4

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This paper cites Varia- tional Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Varia- tional Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning

Reference 5

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Observation d57e1b78-ae1c-4bb1-bbc6-c7e6f4071329 · outbound

This paper cites Analyse des liaisons de probabilité.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Analyse des liaisons de probabilité

Reference 6

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Observation 5c750ad4-d147-42d1-a499-6146bc7e9a4e · outbound

This paper cites A Survey of State Representation Learning for Deep Reinforcement Learning.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning A Survey of State Representation Learning for Deep Reinforcement Learning

Reference 7

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Observation 4e63888f-9e2d-4d4b-ae1c-ac3a3bed76b5 · outbound

This paper cites Diversity is All You Need: Learning Skills without a Reward Function.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Diversity is All You Need: Learning Skills without a Reward Function

Reference 8

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This paper cites Salakhutdi- nov.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Salakhutdi- nov

Reference 9

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This paper cites Contrastive Representations Make Planning Easy.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Contrastive Representations Make Planning Easy

Reference 10

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This paper cites Wichmann.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Wichmann

Reference 11

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This paper cites Variational Intrinsic Control.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Variational Intrinsic Control

Reference 12

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Observation 520a7abc-e7bf-469b-9a29-6bcdf6f4055f · outbound

This paper cites The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA

Reference 13

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This paper cites World Models.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning World Models

Reference 14

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This paper cites Fast Task Inference with Variational Intrinsic Successor Features.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Fast Task Inference with Variational Intrinsic Successor Features

Reference 15

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This paper cites Momentum contrast for unsupervised visual representation learning.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Momentum contrast for unsupervised visual representation learning

Reference 16

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This paper cites Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA

Reference 17

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Observation ea2d45fe-1b1c-429e-a383-e45218786c93 · outbound

This paper cites Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning

Reference 18

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This paper cites Nonlinear independent component analysis: Existence and uniqueness results.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Nonlinear independent component analysis: Existence and uniqueness results

Reference 19

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This paper cites Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA

Reference 20

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This paper cites Occam's Razor for Self Supervised Learning: What is Sufficient to Learn Good Representations?.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Occam's Razor for Self Supervised Learning: What is Sufficient to Learn Good Representations?

Reference 21

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This paper cites Variational Autoencoders and Nonlinear ICA: A Unifying Framework.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Variational Autoencoders and Nonlinear ICA: A Unifying Framework

Reference 22

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This paper cites ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA

Reference 23

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This paper cites Unsupervised Reinforcement Learning with Contrastive Intrinsic Con- trol.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Unsupervised Reinforcement Learning with Contrastive Intrinsic Con- trol

Reference 24

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This paper cites CITRIS: Causal Identifiability from Temporal Intervened Sequences.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning CITRIS: Causal Identifiability from Temporal Intervened Sequences

Reference 25

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This paper cites Asano, Taco Cohen, and Efstratios Gavves.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Asano, Taco Cohen, and Efstratios Gavves

Reference 26

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This paper cites A Single Goal is All You Need: Skills and Exploration Emerge from Contrastive RL without Rewards, Demonstrations, or Subgoals.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning A Single Goal is All You Need: Skills and Exploration Emerge from Contrastive RL without Rewards, Demonstrations, or Subgoals

Reference 27

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This paper cites Self-Supervised Learning via Maximum Entropy Coding.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Self-Supervised Learning via Maximum Entropy Coding

Reference 28

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Unresolved cited work

Reference 29

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This paper cites Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

Reference 30

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This paper cites Weakly-Supervised Disentanglement Without Compromises.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Weakly-Supervised Disentanglement Without Compromises

Reference 31

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This paper cites Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning

Reference 32

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This paper cites Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning

Reference 33

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This paper cites Connectivity-contrastive learning: Combining causal discovery and representation learning for multimodal data.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Connectivity-contrastive learning: Combining causal discovery and representation learning for multimodal data

Reference 34

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This paper cites ALAN: Autonomously Exploring Robotic Agents in the Real World.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning ALAN: Autonomously Exploring Robotic Agents in the Real World

Reference 35

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This paper cites Representation Learning with Contrastive Predictive Coding.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Representation Learning with Contrastive Predictive Coding

Reference 36

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning The Linear Representation Hypothesis and the Geometry of Large Language Models

Reference 37

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Independent Innovation Analysis for Nonlinear Vector Autoregressive Process

Reference 38

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This paper cites Lipschitz- constrained Unsupervised Skill Discovery.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Lipschitz- constrained Unsupervised Skill Discovery

Reference 39

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Lipschitz- constrained unsupervised skill discovery

Reference 40

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning The Geometry of Categorical and Hierarchical Concepts in Large Language Models

Reference 41

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This paper cites METRA: Scalable Unsupervised RL with Metric-Aware Abstraction.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning METRA: Scalable Unsupervised RL with Metric-Aware Abstraction

Reference 42

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Deep Data Density Estimation through Donsker-Varadhan Representation

Reference 43

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This paper cites Controllability-Aware Unsupervised Skill Discovery.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Controllability-Aware Unsupervised Skill Discovery

Reference 44

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Efros, and Trevor Darrell

Reference 45

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This paper cites Causality: Models, Reasoning, and Inference.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Causality: Models, Reasoning, and Inference

Reference 46

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Self-Supervised Exploration via Disagree- ment

Reference 47

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Jacobian-based Causal Discovery with Nonlinear ICA

Reference 48

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This paper cites V ogt, Randall Balestriero, Wieland Brendel, and David Klindt.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning V ogt, Randall Balestriero, Wieland Brendel, and David Klindt

Reference 49

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This paper cites An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis

Reference 50

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This paper cites On Linear Identifiability of Learned Representations.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning On Linear Identifiability of Learned Representations

Reference 51

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This paper cites InfoNCE: Identifying the Gap Between Theory and Practice.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning InfoNCE: Identifying the Gap Between Theory and Practice

Reference 52

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This paper cites Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning

Reference 53

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This paper cites Towards Causal Representation Learning.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Towards Causal Representation Learning

Reference 54

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Planning to Explore via Self-Supervised World Models

Reference 55

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Regularity as Intrinsic Reward for Free Play

Reference 56

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning What Do We Maximize in Self-Supervised Learning?

Reference 57

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning MIT press, 2000

Reference 58

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Dynamics-Aware Unsupervised Discovery of Skills

Reference 59

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Optimistic Active Exploration of Dynamical Systems

Reference 60

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning On Mutual Information Maximization for Representation Learning

Reference 61

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Reinforcement learning: An introduction, volume 1

Reference 62

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This paper cites Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap

Reference 63

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Unsupervised Control Through Non-Parametric Discriminative Rewards

Reference 64

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere

Reference 65

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Correlation and causation

Reference 66

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Observation a32cba91-6d98-48c2-aa65-d3a653bb4ab5 · outbound

This paper cites Yu, and Dahua Lin.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Yu, and Dahua Lin

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:57:04.653934Z

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Causal Component Analysis

Reference 68

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This paper cites Task Adaptation from Skills: Information Geometry, Disentanglement, and New Objectives for Unsupervised Reinforcement Learning.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Task Adaptation from Skills: Information Geometry, Disentanglement, and New Objectives for Unsupervised Reinforcement Learning

Reference 69

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Contrastive Difference Predic- tive Coding, October 2023

Reference 70

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning CausalVAE: Structured Causal Disentanglement in Variational Autoencoder

Reference 71

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Contrastive Learning Inverts the Data Generating Process

Reference 72

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Can a MISL Fly? Analysis and Ingredients for Mutual Information Skill Learning, December 2024

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T15:57:01.022480Z

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Unresolved cited work

Reference 76

Resolution: unresolved; raw_fallback, observed 2026-08-06T15:57:04.469936Z

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Observation 7ae660a5-ec3a-41ce-90ed-91b6a525e1e2 · outbound

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Unresolved cited work

Reference 77

Resolution: unresolved; raw_fallback, observed 2026-08-06T15:57:04.312876Z

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Observation 15b984e2-dcf0-4b3f-b4f8-03845e30e3a6 · outbound

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Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Unresolved cited work

Reference 2019

Resolution: unresolved; raw_fallback, observed 2026-08-06T15:57:05.194691Z

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Observation 5010bf1c-db5d-467d-9b88-6ea248affe83 · outbound

This paper cites 1, 3, 4, 5, 9.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning 1, 3, 4, 5, 9

Reference 2022

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