% Key papers for Learned state and action representations (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2018_recurrent_57553,
  title = {{Recurrent World Models Facilitate Policy Evolution}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11254},
}

@inproceedings{icml2020_curl_37996,
  title = {{CURL: Contrastive Unsupervised Representations for Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6729},
}

@inproceedings{icml2016_control_35773,
  title = {{Control of Memory, Active Perception, and Action in Minecraft}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/oh16.html},
}

@inproceedings{icml2020_leveraging_38419,
  title = {{Leveraging Procedural Generation to Benchmark Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6267},
}

@inproceedings{aaai2018_learning_106005,
  title = {{Learning Structured Representation for Text Classification via Reinforcement Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/12047},
}

@inproceedings{aaai2019_combined_101546,
  title = {{Combined Reinforcement Learning via Abstract Representations}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4238},
}

@inproceedings{iclr2018_eigenoption_28441,
  title = {{Eigenoption Discovery through the Deep Successor Representation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/201},
}

@inproceedings{neurips2020_stochastic_60877,
  title = {{Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18997},
}

@inproceedings{icml2019_deepmdp_37278,
  title = {{DeepMDP: Learning Continuous Latent Space Models for Representation Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4097},
}

@inproceedings{icml2020_bootstrap_37987,
  title = {{Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6472},
}

@inproceedings{neurips2022_pre_64401,
  title = {{Pre-Trained Language Models for Interactive Decision-Making}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54484},
}

@inproceedings{neurips2019_shaping_58983,
  title = {{Shaping Belief States with Generative Environment Models for RL}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14285},
}

@inproceedings{iclr2021_data_30705,
  title = {{Data-Efficient Reinforcement Learning with Self-Predictive Representations}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2797},
}

@inproceedings{aaai2021_domain_461,
  title = {{Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/10452-domain-adaptation-in-reinforcement-learning-via-latent-unified-state-representation/},
}

@inproceedings{aaai2019_the_101642,
  title = {{The Utility of Sparse Representations for Control in Reinforcement Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4349},
}

@inproceedings{icml2020_invariant_38341,
  title = {{Invariant Causal Prediction for Block MDPs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6475},
}

@inproceedings{neurips2021_pretraining_62462,
  title = {{Pretraining Representations for Data-Efficient Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28355},
}

@inproceedings{iclr2019_learning_29011,
  title = {{Learning Finite State Representations of Recurrent Policy Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1062},
}

@inproceedings{iclr2021_contrastive_30655,
  title = {{Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2609},
}

@inproceedings{icml2019_learning_37472,
  title = {{Learning Action Representations for Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3974},
}

@inproceedings{cvpr2024_jointly_23856,
  title = {{Jointly Training and Pruning CNNs via Learnable Agent Guidance and Alignment}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30008},
}

@inproceedings{icml2025_empirical_46046,
  title = {{Empirical Design in Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46712},
}

@inproceedings{icml2025_dino_46542,
  title = {{DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46026},
}

@inproceedings{aaai2024_building_5322,
  title = {{Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29507},
}

@inproceedings{aaai2024_contextual_5487,
  title = {{Contextual Pre-planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28970},
}

@inproceedings{cvpr2024_improving_23784,
  title = {{Improving Unsupervised Hierarchical Representation with Reinforcement Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30509},
}

@inproceedings{neurips2024_state_71525,
  title = {{State Chrono Representation for Enhancing Generalization in Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95738},
}

@inproceedings{aaai2024_using_7734,
  title = {{Using Reinforcement Learning to Iteratively Construct Road Networks from Satellite Images and GPS Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30548},
}

@inproceedings{icml2024_zero_45580,
  title = {{Zero-Shot Reinforcement Learning via Function Encoders}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32872},
}

@inproceedings{icml2024_llm_45734,
  title = {{LLM-Empowered State Representation for Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32718},
}

@inproceedings{icml2024_hieros_44024,
  title = {{Hieros: Hierarchical Imagination on Structured State Space Sequence World Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34428},
}

@inproceedings{icml2024_premier_44110,
  title = {{Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34342},
}

@inproceedings{icml2024_tackling_44576,
  title = {{Tackling Non-Stationarity in Reinforcement Learning via Causal-Origin Representation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33876},
}

@inproceedings{icml2024_dynsyn_45458,
  title = {{DynSyn: Dynamical Synergistic Representation for Efficient Learning and Control in Overactuated Embodied Systems}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32993},
}

@inproceedings{icml2025_sliding_48970,
  title = {{Sliding Puzzles Gym: A Scalable Benchmark for State Representation in Visual Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43653},
}

@inproceedings{icml2026_from_51212,
  title = {{From Representation to Action: A Unified Laplacian Framework for Spatial Representation and Path Planning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=JnROrhQsyJ},
}

@inproceedings{icml2026_squeezing_53482,
  title = {{Squeezing More from the Stream : Learning Representation Online for Streaming Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=fuBIaxqMAG},
}

@inproceedings{neurips2024_the_70397,
  title = {{The Ladder in Chaos: Improving Policy Learning by Harnessing the Parameter Evolving Path in A Low-dimensional Space}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96705},
}

@inproceedings{neurips2025_quantization_75030,
  title = {{Quantization-Free Autoregressive Action Transformer}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=3a18D8IeQ1},
}

@inproceedings{neurips2025_learning_75955,
  title = {{Learning Interactive World Model for Object-Centric Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=E0cjqfM55C},
}