% Key papers for Rich-observation exploration (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2017_contextual_36112,
  title = {{Contextual Decision Processes with low Bellman rank are PAC-Learnable}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/610},
}

@inproceedings{aaai2020_count_102093,
  title = {{Count-Based Exploration with the Successor Representation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5955},
}

@inproceedings{icml2019_provably_37672,
  title = {{Provably Efficient Maximum Entropy Exploration}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4322},
}

@inproceedings{cvpr2017_a_12026,
  title = {{A Reinforcement Learning Approach to the View Planning Problem}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{icml2019_provably_37673,
  title = {{Provably efficient RL with Rich Observations via Latent State Decoding}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3757},
}

@inproceedings{icml2020_kinematic_38353,
  title = {{Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6535},
}

@inproceedings{iclr2018_truncated_28719,
  title = {{TRUNCATED HORIZON POLICY SEARCH: COMBINING REINFORCEMENT LEARNING \& IMITATION LEARNING}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/33},
}

@inproceedings{neurips2020_breaking_59486,
  title = {{Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/16917},
}

@inproceedings{icml2022_guarantees_40643,
  title = {{Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/18295},
}

@inproceedings{neurips2018_on_57463,
  title = {{On Oracle-Efficient PAC RL with Rich Observations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11158},
}

@inproceedings{neurips2019_explicit_58237,
  title = {{Explicit Explore-Exploit Algorithms in Continuous State Spaces}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13975},
}

@inproceedings{aaai2020_an_102106,
  title = {{An Intrinsically-Motivated Approach for Learning Highly Exploring and Fast Mixing Policies}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5968},
}

@inproceedings{aaai2016_efficient_105135,
  title = {{Efficient PAC-Optimal Exploration in Concurrent, Continuous State MDPs with Delayed Updates}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10307},
}

@inproceedings{icml2021_quantum_39843,
  title = {{Quantum algorithms for reinforcement learning with a generative model}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10207},
}

@inproceedings{neurips2016_pac_55929,
  title = {{PAC Reinforcement Learning with Rich Observations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/6918},
}

@inproceedings{neurips2020_improved_60056,
  title = {{Improved Sample Complexity for Incremental Autonomous Exploration in MDPs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18017},
}

@inproceedings{neurips2020_provably_60634,
  title = {{Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/16851},
}

@inproceedings{neurips2022_provable_63728,
  title = {{Provable Defense against Backdoor Policies in Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/52877},
}

@inproceedings{aaai2022_learning_2330,
  title = {{Learning Probably Approximately Complete and Safe Action Models for Stochastic Worlds}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/09795-learning-probably-approximately-complete-and-safe-action-models-for-stochastic-worlds/},
}

@inproceedings{icml2022_reinforcement_41077,
  title = {{Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17599},
}

@inproceedings{iclr2024_achieving_34252,
  title = {{Achieving Sample and Computational Efficient Reinforcement Learning by Action Space Reduction via Grouping}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18823},
}

@inproceedings{icml2024_scalable_43754,
  title = {{Scalable Online Exploration via Coverability}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34698},
}

@inproceedings{icml2024_momentum_45010,
  title = {{Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33442},
}

@inproceedings{neurips2024_the_70705,
  title = {{The Power of Resets in Online Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96419},
}

@inproceedings{icml2026_from_52960,
  title = {{From Generative to Episodic: Sample-Efficient Replicable Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=agN2CgTCIW},
}

@inproceedings{icml2026_improved_53257,
  title = {{Improved Bounds for Reward-Agnostic and Reward-Free Exploration}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=dmTyOwKAmZ},
}

@inproceedings{icml2026_reinforcement_53880,
  title = {{Reinforcement Learning with Action-Triggered Observations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=jhNMY6jHJw},
}

@inproceedings{neurips2024_sample_72176,
  title = {{Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95164},
}

@inproceedings{neurips2025_stochastic_79894,
  title = {{Stochastic Principal-Agent Problems: Computing and Learning Optimal History-Dependent Policies}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=u0rNHqMpFD},
}

@inproceedings{iclr2025_learning_80725,
  title = {{Learning a Fast Mixing Exogenous Block MDP using a Single Trajectory}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/31043},
}

@inproceedings{iclr2026_replicable_85285,
  title = {{Replicable Reinforcement Learning with Linear Function Approximation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=Ccflf8sqjF},
}

@inproceedings{iclr2026_near_86377,
  title = {{Near-Optimal Second-Order Guarantees for Model-Based Adversarial Imitation Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=PD8wnZOV1J},
}

@inproceedings{neurips2026_hitting_93522,
  title = {{Hitting Time Isomorphism for Multi-Stage Planning with Foundation Policies}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/152292},
}

@inproceedings{iclr2026_instance_89301,
  title = {{Instance-Dependent Fixed-Budget Pure Exploration in Reinforcement Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=xIycolc5Xw},
}