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

@inproceedings{iclr2019_exploration_28881,
  title = {{Exploration by random network distillation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1093},
}

@inproceedings{icml2018_gep_36709,
  title = {{GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2151},
}

@inproceedings{iclr2020_never_29709,
  title = {{Never Give Up: Learning Directed Exploration Strategies}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=Sye57xStvB},
}

@inproceedings{neurips2018_diversity_57064,
  title = {{Diversity-Driven Exploration Strategy for Deep Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11992},
}

@inproceedings{neurips2017_ex2_56279,
  title = {{EX2: Exploration with Exemplar Models for Deep Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9044},
}

@inproceedings{iclr2019_contingency_28989,
  title = {{Contingency-Aware Exploration in Reinforcement Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/695},
}

@inproceedings{iclr2019_learning_29083,
  title = {{Learning Exploration Policies for Navigation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/883},
}

@inproceedings{iclr2020_ride_29829,
  title = {{RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=rkg-TJBFPB},
}

@inproceedings{iclr2020_learning_29787,
  title = {{Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=rJgJDAVKvB},
}

@inproceedings{icml2019_emi_37322,
  title = {{EMI: Exploration with Mutual Information}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4026},
}

@inproceedings{iclr2020_exploration_29671,
  title = {{Exploration in Reinforcement Learning with Deep Covering Options}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=SkeIyaVtwB},
}

@inproceedings{icml2021_fast_39338,
  title = {{Fast active learning for pure exploration in reinforcement learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8541},
}

@inproceedings{icml2019_curiosity_37259,
  title = {{Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3829},
}

@inproceedings{iclr2020_on_29285,
  title = {{On Bonus Based Exploration Methods In The Arcade Learning Environment}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=BJewlyStDr},
}

@inproceedings{aaai2021_exploration_568,
  title = {{Exploration by Maximizing Renyi Entropy for Reward-Free RL Framework}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/10859-exploration-by-maximizing-renyi-entropy-for-reward-free-rl-framework/},
}

@inproceedings{icml2021_state_39980,
  title = {{State Entropy Maximization with Random Encoders for Efficient Exploration}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9857},
}

@inproceedings{neurips2021_noveld_61639,
  title = {{NovelD: A Simple yet Effective Exploration Criterion}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26335},
}

@inproceedings{icml2019_discovering_37297,
  title = {{Discovering Options for Exploration by Minimizing Cover Time}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4279},
}

@inproceedings{neurips2022_improving_64193,
  title = {{Improving Intrinsic Exploration with Language Abstractions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53930},
}

@inproceedings{icml2019_self_37731,
  title = {{Self-Supervised Exploration via Disagreement}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4234},
}

@inproceedings{aaai2024_explore_5911,
  title = {{Explore 3D Dance Generation via Reward Model from Automatically-Ranked Demonstrations}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/27783},
}

@inproceedings{icml2024_exploration_45491,
  title = {{Exploration and Anti-Exploration with Distributional Random Network Distillation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32960},
}

@inproceedings{neurips2024_effective_71030,
  title = {{Effective Exploration Based on the Structural Information Principles}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96179},
}

@inproceedings{aaai2024_abstract_5108,
  title = {{Abstract and Explore: A Novel Behavioral Metric with Cyclic Dynamics in Reinforcement Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29660},
}

@inproceedings{icml2025_hyper_48495,
  title = {{Hyper: Hyperparameter Robust Efficient Exploration in Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44124},
}

@inproceedings{iclr2025_maxinforl_82127,
  title = {{MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29662},
}

@inproceedings{iclr2024_the_34235,
  title = {{The Curse of Diversity in Ensemble-Based Exploration}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18840},
}

@inproceedings{iclr2024_unlocking_34352,
  title = {{Unlocking the Power of Representations in Long-term Novelty-based Exploration}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18723},
}

@inproceedings{iclr2024_improving_34655,
  title = {{Improving Intrinsic Exploration by Creating Stationary Objectives}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18419},
}

@inproceedings{icml2024_random_44669,
  title = {{Random Latent Exploration for Deep Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33783},
}

@inproceedings{icml2025_exploring_46699,
  title = {{Exploring Large Action Sets with Hyperspherical Embeddings using von Mises-Fisher Sampling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45873},
}

@inproceedings{icml2025_kea_47632,
  title = {{KEA: Keeping Exploration Alive by Proactively Coordinating Exploration Strategies}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44965},
}

@inproceedings{icml2025_sensei_47730,
  title = {{SENSEI: Semantic Exploration Guided by Foundation Models to Learn Versatile World Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44870},
}

@inproceedings{icml2026_task_50702,
  title = {{Task-Aware Exploration via a Predictive Bisimulation Metric}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=EyxlSrJWT5},
}

@inproceedings{icml2026_laser_53691,
  title = {{LASER: Learning Active Sensing for Continuum Field Reconstruction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=hnoSRifw1F},
}

@inproceedings{neurips2024_leveraging_71967,
  title = {{Leveraging Separated World Model for Exploration in Visually Distracted Environments}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95344},
}

@inproceedings{neurips2025_off_76429,
  title = {{Off-policy Reinforcement Learning with Model-based Exploration Augmentation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=JGkZgEEjiM},
}

@inproceedings{neurips2025_novel_80312,
  title = {{Novel Exploration via Orthogonality}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=yJS1eZSNUv},
}

@inproceedings{iclr2025_episodic_81587,
  title = {{Episodic Novelty Through Temporal Distance}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30190},
}

@inproceedings{iclr2025_a_84027,
  title = {{A Single Goal is All You Need: Skills and Exploration Emerge from Contrastive RL without Rewards, Demonstrations, or Subgoals}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/27798},
}