Open Problem: Active Representation Learning
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Abstract (arXiv)
In this work, we introduce the concept of Active Representation Learning, a novel class of problems that intertwines exploration and representation learning within partially observable environments. We extend ideas from Active Simultaneous Localization and Mapping (active SLAM), and translate them to scientific discovery problems, exemplified by adaptive microscopy. We explore the need for a framework that derives exploration skills from representations that are in some sense actionable, aiming to enhance the efficiency and effectiveness of data collection and model building in the natural sciences.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 33 of its 56 references that are in the map. First posted on arXiv 6 Jun 2024.
Builds on
33 of 56 references.
References · 33
- 2019Dream to Control: Learning Behaviors by Latent Imaginationcited by 1,598
- 2018Learning Latent Dynamics for Planning from Pixelscited by 1,221
- 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,173
- 2020CURL: Contrastive Unsupervised Representations for Reinforcement Learningcited by 745
- 2018Diversity is All You Need: Learning Skills without a Reward Functioncited by 734
- 2020Reinforcement Learning with Augmented Datacited by 452
- 2020Object Goal Navigation using Goal-Oriented Semantic Explorationcited by 421
- 2016Successor Features for Transfer in Reinforcement Learningcited by 331
- 2020Learning to Explore using Active Neural SLAMcited by 308
- 2020Decoupling Representation Learning from Reinforcement Learningcited by 263
- 2019REMIND Your Neural Network to Prevent Catastrophic Forgettingcited by 197
- 2021Reinforcement Learning with Prototypical Representationscited by 179
21 more
- 2019Continual Unsupervised Representation Learningcited by 147
- 2021URLB: Unsupervised Reinforcement Learning Benchmarkcited by 138
- 2019Fast Task Inference with Variational Intrinsic Successor Featurescited by 131
- 2018Memory Efficient Experience Replay for Streaming Learningcited by 128
- 2018Variational Option Discovery Algorithmscited by 121
- 2022Learnable latent embeddings for joint behavioral and neural analysiscited by 113
- 2021APS: Active Pretraining with Successor Featurescited by 109
- 2017Eigenoption Discovery through the Deep Successor Representationcited by 85
- 2022CIC: Contrastive Intrinsic Control for Unsupervised Skill Discoverycited by 66
- 2022A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontierscited by 62
- 2020Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learningcited by 53
- 2020Impact of Representation Learning in Linear Banditscited by 51
- 2023Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learningcited by 48
- 2020Semantic Curiosity for Active Visual Learningcited by 44
- 2022Provable Benefits of Representational Transfer in Reinforcement Learningcited by 38
- 2021Online Learning with Optimism and Delaycited by 33
- 2023Deep Laplacian-based Options for Temporally-Extended Explorationcited by 28
- 2022Multi-task Representation Learning with Stochastic Linear Banditscited by 20
- 2020Weakly-Supervised Reinforcement Learning for Controllable Behaviorcited by 17
- 2021Online Unsupervised Learning of Visual Representations and Categoriescited by 4
- 2018Disentangling Controllable and Uncontrollable Factors of Variation by Interacting with the Worldcited by 3
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