% Key papers for Distributional value learning (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{aaai2018_distributional_102524,
  title = {{Distributional Reinforcement Learning With Quantile Regression}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11791},
}

@inproceedings{icml2018_implicit_36743,
  title = {{Implicit Quantile Networks for Distributional Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2450},
}

@inproceedings{icml2017_a_36033,
  title = {{A Distributional Perspective on Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/580},
}

@inproceedings{icml2016_cumulative_35782,
  title = {{Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/la16.html},
}

@inproceedings{icml2020_controlling_38051,
  title = {{Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6846},
}

@inproceedings{aaai2019_a_101657,
  title = {{A Comparative Analysis of Expected and Distributional Reinforcement Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4365},
}

@inproceedings{neurips2019_fully_58303,
  title = {{Fully Parameterized Quantile Function for Distributional Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13735},
}

@inproceedings{icml2019_distributional_37307,
  title = {{Distributional Reinforcement Learning for Efficient Exploration}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4297},
}

@inproceedings{icml2019_statistics_37758,
  title = {{Statistics and Samples in Distributional Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3583},
}

@inproceedings{aaai2021_distributional_451,
  title = {{Distributional Reinforcement Learning via Moment Matching}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/09144-distributional-reinforcement-learning-via-moment-matching/},
}

@inproceedings{neurips2020_a_59255,
  title = {{A Local Temporal Difference Code for Distributional Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18766},
}

@inproceedings{neurips2020_non_60432,
  title = {{Non-Crossing Quantile Regression for Distributional Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17686},
}

@inproceedings{icml2019_nonlinear_37583,
  title = {{Nonlinear Distributional Gradient Temporal-Difference Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3628},
}

@inproceedings{aaai2021_bayesian_188,
  title = {{Bayesian Distributional Policy Gradients}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/08429-bayesian-distributional-policy-gradients/},
}

@inproceedings{icml2019_distributional_37306,
  title = {{Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3848},
}

@inproceedings{icml2022_lyapunov_40800,
  title = {{Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/18219},
}

@inproceedings{neurips2021_distributional_63384,
  title = {{Distributional Reinforcement Learning for Multi-Dimensional Reward Functions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26980},
}

@inproceedings{neurips2023_distributional_69526,
  title = {{Distributional Pareto-Optimal Multi-Objective Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/70393},
}

@inproceedings{aaai2024_beyond_5283,
  title = {{Beyond Expected Return: Accounting for Policy Reproducibility When Evaluating Reinforcement Learning Algorithms}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29090},
}

@inproceedings{icml2022_distributional_40467,
  title = {{Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17075},
}

@inproceedings{neurips2024_distributional_71088,
  title = {{Distributional Reinforcement Learning with Regularized Wasserstein Loss}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96126},
}

@inproceedings{icml2024_single_43375,
  title = {{Single-Trajectory Distributionally Robust Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35077},
}

@inproceedings{icml2024_distributional_45122,
  title = {{Distributional Bellman Operators over Mean Embeddings}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33330},
}

@inproceedings{neurips2024_action_71016,
  title = {{Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96191},
}

@inproceedings{neurips2024_foundations_72877,
  title = {{Foundations of Multivariate Distributional Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94528},
}

@inproceedings{iclr2024_diverse_35348,
  title = {{Diverse Projection Ensembles for Distributional Reinforcement Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17725},
}

@inproceedings{icml2024_averaging_45138,
  title = {{Averaging \$n\$-step Returns Reduces Variance in Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33314},
}

@inproceedings{icml2024_a_45825,
  title = {{A Distributional Analogue to the Successor Representation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32627},
}

@inproceedings{icml2025_beyond_47594,
  title = {{Beyond CVaR: Leveraging Static Spectral Risk Measures for Enhanced Decision-Making in Distributional Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45002},
}

@inproceedings{icml2025_categorical_48057,
  title = {{Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44550},
}

@inproceedings{icml2026_path_51782,
  title = {{Path-Coupled Bellman Flows for Distributional Reinforcement Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Ps58JvpLT7},
}

@inproceedings{icml2026_multivariate_55402,
  title = {{Multivariate Distributional Reinforcement Learning Using Sliced Divergences}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=yeyUprQtAY},
}

@inproceedings{neurips2024_near_71558,
  title = {{Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95709},
}

@inproceedings{neurips2024_adversarially_72520,
  title = {{Adversarially Robust Decision Transformer}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94846},
}

@inproceedings{neurips2025_conformal_77199,
  title = {{Conformal Prediction Beyond the Horizon: Distribution-Free Inference for Policy Evaluation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=RIkHzQbpeR},
}

@inproceedings{neurips2025_intrinsic_79724,
  title = {{Intrinsic Benefits of Categorical Distributional Loss: Uncertainty-aware Regularized Exploration in Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=sORaSF9Uxo},
}

@inproceedings{iclr2026_value_84424,
  title = {{Value Flows}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=2VyNYUVF2k},
}

@inproceedings{neurips2026_a_89986,
  title = {{A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/149942},
}

@inproceedings{neurips2026_quotient_91060,
  title = {{Quotient-Categorical Representations for Bellman-Compatible Average-Reward Distributional Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/149377},
}