% Key papers for Latent-space world models (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2016_benchmarking_35753,
  title = {{Benchmarking Deep Reinforcement Learning for Continuous Control}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/duan16.html},
}

@inproceedings{aaai2018_feature_102411,
  title = {{Feature Engineering for Predictive Modeling Using Reinforcement Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11678},
}

@inproceedings{icml2019_solar_37714,
  title = {{SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4307},
}

@inproceedings{neurips2019_adaptive_57866,
  title = {{Adaptive Auxiliary Task Weighting for Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13624},
}

@inproceedings{icml2018_efficient_36663,
  title = {{Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2242},
}

@inproceedings{neurips2017_value_56761,
  title = {{Value Prediction Network}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9383},
}

@inproceedings{icml2021_reinforcement_39868,
  title = {{Reinforcement Learning with Prototypical Representations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10423},
}

@inproceedings{neurips2020_predictive_60604,
  title = {{Predictive Information Accelerates Learning in RL}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18455},
}

@inproceedings{icml2020_context_38043,
  title = {{Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6363},
}

@inproceedings{iclr2020_discriminative_29467,
  title = {{Discriminative Particle Filter Reinforcement Learning for Complex Partial observations}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=HJl8_eHYvS},
}

@inproceedings{icml2019_task_37785,
  title = {{Task-Agnostic Dynamics Priors for Deep Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4136},
}

@inproceedings{icml2018_deep_36626,
  title = {{Deep Variational Reinforcement Learning for POMDPs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2456},
}

@inproceedings{iclr2023_erl_32297,
  title = {{ERL-Re\$^2\$: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy Representation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/12045},
}

@inproceedings{icml2020_can_37999,
  title = {{Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6387},
}

@inproceedings{neurips2021_robust_62259,
  title = {{Robust Predictable Control}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27134},
}

@inproceedings{neurips2020_bridging_59487,
  title = {{Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18918},
}

@inproceedings{iclr2021_control_30485,
  title = {{Control-Aware Representations for Model-based Reinforcement Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2582},
}

@inproceedings{iclr2023_modem_32388,
  title = {{MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11332},
}

@inproceedings{neurips2021_which_61428,
  title = {{Which Mutual-Information Representation Learning Objectives are Sufficient for Control?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28515},
}

@inproceedings{neurips2021_playvirtual_61778,
  title = {{PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26236},
}

@inproceedings{iclr2024_predictive_34716,
  title = {{Predictive auxiliary objectives in deep RL mimic learning in the brain}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18358},
}

@inproceedings{iclr2024_multi_34709,
  title = {{Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18365},
}

@inproceedings{iclr2024_understanding_35430,
  title = {{Understanding when Dynamics-Invariant Data Augmentations Benefit Model-free Reinforcement Learning Updates}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17643},
}

@inproceedings{icml2024_cross_43408,
  title = {{Cross-Domain Policy Adaptation by Capturing Representation Mismatch}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35044},
}

@inproceedings{icml2024_harmonydream_45722,
  title = {{HarmonyDream: Task Harmonization Inside World Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32730},
}

@inproceedings{icml2025_stealing_47771,
  title = {{Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44829},
}

@inproceedings{neurips2024_reinforcement_71903,
  title = {{Reinforcement Learning with Euclidean Data Augmentation for State-Based Continuous Control}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95401},
}

@inproceedings{neurips2024_policy_73407,
  title = {{Policy-shaped prediction: avoiding distractions in model-based reinforcement learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94051},
}

@inproceedings{neurips2024_reinforcement_74046,
  title = {{Reinforcement Learning Under Latent Dynamics: Toward Statistical and Algorithmic Modularity}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93479},
}

@inproceedings{neurips2025_dymodreamer_77317,
  title = {{DyMoDreamer: World Modeling with Dynamic Modulation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=SYKwGnik3w},
}

@inproceedings{iclr2025_causal_83398,
  title = {{Causal Information Prioritization for Efficient Reinforcement Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28418},
}

@inproceedings{icml2024_learning_43752,
  title = {{Learning Latent Dynamic Robust Representations for World Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34700},
}

@inproceedings{icml2024_rich_43965,
  title = {{Rich-Observation Reinforcement Learning with Continuous Latent Dynamics}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34487},
}

@inproceedings{icml2024_efficientzero_44148,
  title = {{EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34304},
}

@inproceedings{icml2024_an_44462,
  title = {{An Information Theoretic Approach to Interaction-Grounded Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33990},
}

@inproceedings{icml2025_maximum_46295,
  title = {{Maximum Total Correlation Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46265},
}

@inproceedings{icml2025_sold_47635,
  title = {{SOLD: Slot Object-Centric Latent Dynamics Models for Relational Manipulation Learning from Pixels}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44962},
}

@inproceedings{neurips2024_unlock_73139,
  title = {{Unlock the Intermittent Control Ability of Model Free Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94291},
}

@inproceedings{neurips2025_composite_75401,
  title = {{Composite Flow Matching for Reinforcement Learning with Shifted-Dynamics Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=7cPDOBWTbM},
}

@inproceedings{iclr2026_ego_84792,
  title = {{Ego-Foresight: Self-supervised Learning of Agent-Aware Representations for Improved RL}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=6itufi98Q3},
}