% Key papers for Hierarchical skills and priors (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2016_hierarchical_55767,
  title = {{Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7266},
}

@inproceedings{iclr2018_parameter_28460,
  title = {{Parameter Space Noise for Exploration}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/228},
}

@inproceedings{icml2017_modular_36290,
  title = {{Modular Multitask Reinforcement Learning with Policy Sketches}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/548},
}

@inproceedings{iclr2017_stochastic_28210,
  title = {{Stochastic Neural Networks for Hierarchical Reinforcement Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=B1oK8aoxe},
}

@inproceedings{neurips2018_improving_57229,
  title = {{Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11492},
}

@inproceedings{aaai2020_reinforcement_102224,
  title = {{Reinforcement Learning with Perturbed Rewards}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6086},
}

@inproceedings{iclr2020_dynamics_29461,
  title = {{Dynamics-Aware Unsupervised Discovery of Skills}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=HJgLZR4KvH},
}

@inproceedings{icml2018_latent_36776,
  title = {{Latent Space Policies for Hierarchical Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2334},
}

@inproceedings{neurips2020_effective_59756,
  title = {{Effective Diversity in Population Based Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17049},
}

@inproceedings{neurips2019_hierarchical_58369,
  title = {{Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13359},
}

@inproceedings{neurips2019_the_59069,
  title = {{The Option Keyboard: Combining Skills in Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14248},
}

@inproceedings{iclr2019_hierarchical_28983,
  title = {{Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1109},
}

@inproceedings{iclr2020_fast_29278,
  title = {{Fast Task Inference with Variational Intrinsic Successor Features}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=BJeAHkrYDS},
}

@inproceedings{iclr2020_sub_29364,
  title = {{Sub-policy Adaptation for Hierarchical Reinforcement Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=ByeWogStDS},
}

@inproceedings{icml2021_pebble_39766,
  title = {{PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8487},
}

@inproceedings{neurips2022_causality_66065,
  title = {{Causality-driven Hierarchical Structure Discovery for Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54290},
}

@inproceedings{iclr2021_smirl_30467,
  title = {{SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3047},
}

@inproceedings{iclr2019_hierarchical_29050,
  title = {{Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1042},
}

@inproceedings{aaai2023_incremental_3904,
  title = {{Incremental Reinforcement Learning with Dual-Adaptive ε-Greedy Exploration}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25899},
}

@inproceedings{iclr2021_reset_30157,
  title = {{Reset-Free Lifelong Learning with Skill-Space Planning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3326},
}

@inproceedings{aaai2024_robust_7176,
  title = {{Robust Policy Learning via Offline Skill Diffusion}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29217},
}

@inproceedings{aaai2025_enhancing_8987,
  title = {{Enhancing Question Generation through Diversity-Seeking Reinforcement Learning with Bilevel Policy Decomposition}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34693},
}

@inproceedings{iclr2024_proximal_34493,
  title = {{Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18581},
}

@inproceedings{aaai2024_dgpo_5560,
  title = {{DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29019},
}

@inproceedings{aaai2025_noise_10123,
  title = {{Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34280},
}

@inproceedings{icml2024_acquiring_43650,
  title = {{Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34802},
}

@inproceedings{iclr2024_dreamsmooth_34061,
  title = {{DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19014},
}

@inproceedings{iclr2024_in_35493,
  title = {{In-context Exploration-Exploitation for Reinforcement Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17580},
}

@inproceedings{iclr2024_pre_35667,
  title = {{Pre-Training and Fine-Tuning Generative Flow Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17406},
}

@inproceedings{iclr2025_efficient_81613,
  title = {{Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30164},
}

@inproceedings{iclr2025_qmp_82632,
  title = {{QMP: Q-switch Mixture of Policies for Multi-Task Behavior Sharing}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29165},
}

@inproceedings{iclr2026_learn_86010,
  title = {{Learn the Ropes, Then Trust the Wins: Self-imitation with Progressive Exploration for Agentic Reinforcement Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=Kssko33Ekq},
}

@inproceedings{iclr2024_proper_33725,
  title = {{Proper Laplacian Representation Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19350},
}

@inproceedings{iclr2024_sample_34653,
  title = {{Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18421},
}

@inproceedings{iclr2024_coplanner_35066,
  title = {{COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18007},
}

@inproceedings{icml2024_when_43373,
  title = {{When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal Abstractions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35079},
}

@inproceedings{icml2024_learning_43952,
  title = {{Learning to Scale Logits for Temperature-Conditional GFlowNets}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34500},
}

@inproceedings{icml2026_avoid_53216,
  title = {{Avoid What You Know: Divergent Trajectory Balance for GFlowNets}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=dPDOQs7qHl},
}

@inproceedings{neurips2025_sombrl_78403,
  title = {{SOMBRL: Scalable and Optimistic Model-Based RL}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=eGfi5k7RP6},
}

@inproceedings{neurips2025_policy_80254,
  title = {{Policy Compatible Skill Incremental Learning via Lazy Learning Interface}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=xmYT1JqVpj},
}