@article{zhang2023distributional,
  title={Distributional GFlowNets with Quantile Flows},
  author={Zhang, Dinghuai and Pan, Ling and Chen, Ricky TQ and Courville, Aaron and Bengio, Yoshua},
  journal={arXiv preprint arXiv:2302.05793},
  year={2023}
}
@book{baars1993cognitive,
  title={A cognitive theory of consciousness},
  author={Baars, Bernard J},
  year={1993},
  publisher={Cambridge University Press}
}
@article{metropolis1953equation,
  title={Equation of state calculations by fast computing machines},
  author={Metropolis, Nicholas and Rosenbluth, Arianna W and Rosenbluth, Marshall N and Teller, Augusta H and Teller, Edward},
  journal={The journal of chemical physics},
  volume={21},
  number={6},
  pages={1087--1092},
  year={1953},
  publisher={American Institute of Physics}
}
@article{vaswani2017attention,
  title={Attention is all you need},
  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
  journal={Neural Information Processing Systems (NIPS)},
  year={2017}
}
@article{kingma2014adam,
  title={Adam: A method for stochastic optimization},
  author={Kingma, Diederik P and Ba, Jimmy},
  journal={International Conference on Learning Representations (ICLR)},
  year={2015}
}
@article{ren2021learning,
  title={Learning Long-Term Reward Redistribution via Randomized Return Decomposition},
  author={Ren, Zhizhou and Guo, Ruihan and Zhou, Yuan and Peng, Jian},
  journal={International Conference on Learning Representations (ICLR)},
  year={2022}
}
@article{cowan2010magical,
  title={The magical mystery four: How is working memory capacity limited, and why?},
  author={Cowan, Nelson},
  journal={Current directions in psychological science},
  volume={19},
  number={1},
  pages={51--57},
  year={2010},
  publisher={Sage Publications Sage CA: Los Angeles, CA}
}
@misc{gfn-tutorial2022,
 title={The {GFlowNet} {T}utorial},
author = {Yoshua Bengio and Nikolay Malkin and Moksh Jain},
howpublished = {https://milayb.notion.site/The-GFlowNet-Tutorial-95434ef0e2d94c24aab90e69b30be9b3},
year = 2022,
}
@article{Zhang2022LatentSM,
  title={Latent State Marginalization as a Low-cost Approach for Improving Exploration},
  author={Dinghuai Zhang and Aaron C. Courville and Yoshua Bengio and Qinqing Zheng and Amy Zhang and Ricky T. Q. Chen},
  journal={ArXiv},
  year={2022},
  volume={abs/2210.00999}
}
@article{salakhutdinov2009learning,
  title={Learning in Markov random fields using tempered transitions},
  author={Salakhutdinov, Ruslan},
  journal={Neural Information Processing Systems (NIPS)},
  year={2009}
}
@article{bengio2013better,
  title={Better mixing via deep representations},
  author={Bengio, Yoshua and Mesnil, Gr{\'e}goire and Dauphin, Yann and Rifai, Salah},
  journal={International Conference on Machine Learning (ICML)},
  year={2013}
}
@article{heess2015learning,
  title={Learning continuous control policies by stochastic value gradients},
  author={Heess, Nicolas and Wayne, Gregory and Silver, David and Lillicrap, Timothy and Erez, Tom and Tassa, Yuval},
  journal={Advances in neural information processing systems},
  volume={28},
  year={2015}
}
@article{schrittwieser2020mastering,
  title={Mastering atari, go, chess and shogi by planning with a learned model},
  author={Schrittwieser, Julian and Antonoglou, Ioannis and Hubert, Thomas and Simonyan, Karen and Sifre, Laurent and Schmitt, Simon and Guez, Arthur and Lockhart, Edward and Hassabis, Demis and Graepel, Thore and others},
  journal={Nature},
  volume={588},
  number={7839},
  pages={604--609},
  year={2020},
  publisher={Nature Publishing Group UK London}
}
@article{hafner2019dream,
  title={Dream to control: Learning behaviors by latent imagination},
  author={Hafner, Danijar and Lillicrap, Timothy and Ba, Jimmy and Norouzi, Mohammad},
  journal={arXiv preprint arXiv:1912.01603},
  year={2019}
}
@inproceedings{venkatraman2015improving,
  title={Improving multi-step prediction of learned time series models},
  author={Venkatraman, Arun and Hebert, Martial and Bagnell, J},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={29},
  number={1},
  year={2015}
}

@article{paster2022you,
  title={You Can't Count on Luck: Why Decision Transformers Fail in Stochastic Environments},
  author={Paster, Keiran and McIlraith, Sheila and Ba, Jimmy},
  journal={arXiv preprint arXiv:2205.15967},
  year={2022}
}
@article{goodfellow2014generative,
title = {Generative Adversarial Nets},
author = {Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
journal = {Neural Information Processing Systems (NIPS)},
pages = {2672--2680},
year = {2014},
}
@article{ho2020denoising,
  title={Denoising diffusion probabilistic models},
  author={Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={6840--6851},
  year={2020}
}
@book{goodfellow2016deep,
  title={Deep learning},
  author={Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron},
  year={2016},
  publisher={MIT press}
}
@article{baddeley1992working,
  title={Working memory},
  author={Baddeley, Alan},
  journal={Science},
  volume={255},
  number={5044},
  pages={556--559},
  year={1992},
  publisher={American Association for the Advancement of Science}
}
@article{cowan1999embedded,
  title={An embedded-processes model of working memory.},
  author={Cowan, Nelson},
  year={1999},
  publisher={Cambridge University Press}
}
@article{hastings1970monte,
  title={Monte Carlo sampling methods using Markov chains and their applications},
  author={Hastings, W Keith},
  year={1970},
  publisher={Oxford University Press}
}
@article{kingma2013auto,
  title={Auto-encoding variational bayes},
  author={Kingma, Diederik P and Welling, Max},
  journal={arXiv preprint arXiv:1312.6114},
  year={2013}
}
@article{goodfellow2020generative,
  title={Generative adversarial networks},
  author={Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
  journal={Communications of the ACM},
  volume={63},
  number={11},
  pages={139--144},
  year={2020},
  publisher={ACM New York, NY, USA}
}
@article{andrieu2003introduction,
  title={An introduction to MCMC for machine learning},
  author={Andrieu, Christophe and De Freitas, Nando and Doucet, Arnaud and Jordan, Michael I},
  journal={Machine learning},
  volume={50},
  number={1},
  pages={5--43},
  year={2003},
  publisher={Springer}
}
@article{shanahan2006cognitive,
  title={A cognitive architecture that combines internal simulation with a global workspace},
  author={Shanahan, Murray},
  journal={Consciousness and cognition},
  volume={15},
  number={2},
  pages={433--449},
  year={2006},
  publisher={Elsevier}
}
@book{shanahan2010embodiment,
  title={Embodiment and the inner life: Cognition and Consciousness in the Space of Possible Minds},
  author={Shanahan, Murray},
  year={2010},
  publisher={Oxford University Press, USA}
}
@article{shanahan2012brain,
  title={The brain's connective core and its role in animal cognition},
  author={Shanahan, Murray},
  journal={Philosophical Transactions of the Royal Society B: Biological Sciences},
  volume={367},
  number={1603},
  pages={2704--2714},
  year={2012},
  publisher={The Royal Society}
}
@article{dehaene2017consciousness,
  title={What is consciousness, and could machines have it?},
  author={Dehaene, Stanislas and Lau, Hakwan and Kouider, Sid},
  journal={Science},
  volume={358},
  number={6362},
  pages={486--492},
  year={2017},
  publisher={American Association for the Advancement of Science}
}
@article{dehaene1998neuronal,
  title={A neuronal model of a global workspace in effortful cognitive tasks},
  author={Dehaene, Stanislas and Kerszberg, Michel and Changeux, Jean-Pierre},
  journal={Proceedings of the national Academy of Sciences},
  volume={95},
  number={24},
  pages={14529--14534},
  year={1998},
  publisher={National Acad Sciences}
}
@article{shanahan2005applying,
  title={Applying global workspace theory to the frame problem},
  author={Shanahan, Murray and Baars, Bernard},
  journal={Cognition},
  volume={98},
  number={2},
  pages={157--176},
  year={2005},
  publisher={Elsevier}
}
@article{Le2019RevisitingRW,
  title={Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow},
  author={Tuan Anh Le and Adam R. Kosiorek and N. Siddharth and Yee Whye Teh and Frank Wood},
  journal={Neural Information Processing Systems (NeurIPS)},
  year={2019}
}
@article{Le2022HybridMW,
  title={Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface},
  author={Tuan Anh Le and Katherine M. Collins and Luke B. Hewitt and Kevin Ellis and N. Siddharth and Samuel J. Gershman and Joshua B. Tenenbaum},
  journal={International Conference on Learning Representations (ICLR)},
  year={2022}
}
@article{Hewitt2020LearningTL,
  title={Learning to learn generative programs with Memoised Wake-Sleep},
  author={Luke B. Hewitt and Tuan Anh Le and Joshua B. Tenenbaum},
  journal={Uncertainty in Artificial Intelligence (UAI)},
  year={2020}
}
@article{Hinton1995TheA,
  title={The ``wake-sleep'' algorithm for unsupervised neural networks.},
  author={Geoffrey E. Hinton and Peter Dayan and Brendan J. Frey and R M Neal},
  journal={Science},
  year={1995},
  volume={268 5214},
  pages={
          1158-61
        }
}
@article{Ranganath2016OperatorVI,
  title={Operator Variational Inference},
  author={Rajesh Ranganath and Dustin Tran and Jaan Altosaar and David M. Blei},
  journal={Neural Information Processing Systems (NIPS)},
  year={2016}
}
@article{Liu2016SteinVG,
  title={Stein Variational Gradient Descent: A General Purpose {Bayesian} Inference Algorithm},
  author={Qiang Liu and Dilin Wang},
  journal={Neural Information Processing Systems (NIPS)},
  year={2016}
}
@article{HernndezLobato2016BlackBoxAD,
  title={Black-Box Alpha Divergence Minimization},
  author={Jos{\'e} Miguel Hern{\'a}ndez-Lobato and Yingzhen Li and Mark Rowland and Thang D. Bui and Daniel Hern{\'a}ndez-Lobato and Richard E. Turner},
  journal={International Conference on Machine Learning (ICML)},
  year={2016}
}
@article{Dieng2017VariationalIV,
  title={Variational Inference via $\chi$ Upper Bound Minimization},
  author={Adji Bousso Dieng and Dustin Tran and Rajesh Ranganath and John William Paisley and David M. Blei},
  journal={Neural Information Processing Systems (NIPS)},
  year={2017}
}
@article{Li2015StochasticEP,
  title={Stochastic Expectation Propagation},
  author={Yingzhen Li and Jos{\'e} Miguel Hern{\'a}ndez-Lobato and Richard E. Turner},
  journal={Neural Information Processing Systems (NIPS)},
  year={2015}
}
@article{Minka2001ExpectationPF,
  title={Expectation Propagation for approximate {Bayesian} inference},
  author={Thomas P. Minka},
  journal={arXiv preprint 1301.2294},
  year={2001}
}
@inproceedings{Minka2005DivergenceMA,
  title={Divergence measures and message passing},
  author={Thomas P. Minka},
  year={2005}
}
@article{Wang2018VariationalIW,
  title={Variational Inference with Tail-adaptive f-Divergence},
  author={Dilin Wang and Hao Liu and Qiang Liu},
  journal={Neural Information Processing Systems (NeurIPS)},
  year={2018},
}
@article{Rainforth2018TighterVB,
  title={Tighter Variational Bounds are Not Necessarily Better},
  author={Tom Rainforth and Adam R. Kosiorek and Tuan Anh Le and Chris J. Maddison and Maximilian Igl and Frank Wood and Yee Whye Teh},
  journal={International Conference on Machine Learning (ICML)},
  year={2018}
}
@article{Domke2018ImportanceWA,
  title={Importance Weighting and Variational Inference},
  author={Justin Domke and Daniel Sheldon},
  journal={Neural Information Processing Systems (NeurIPS)},
  year={2018}
}
@article{Masrani2019TheTV,
  title={The Thermodynamic Variational Objective},
  author={Vaden Masrani and Tuan Anh Le and Frank D. Wood},
  journal={Neural Information Processing Systems (NeurIPS)},
  year={2019}
}
@article{Gregor2014DeepAN,
  title={Deep {AutoRegressive} Networks},
  author={Karol Gregor and Ivo Danihelka and Andriy Mnih and Charles Blundell and Daan Wierstra},
  journal={International Conference on Machine Learning (ICML)},
  year={2014}
}
@article{Grathwohl2018BackpropagationTT,
  title={Backpropagation through the Void: Optimizing control variates for black-box gradient estimation},
  author={Will Grathwohl and Dami Choi and Yuhuai Wu and Geoffrey Roeder and David Kristjanson Duvenaud},
  journal={International Conference on Learning Representations (ICLR)},
  year={2018}
}

@article{Tucker2017REBARLU,
  title={{REBAR}: Low-variance, unbiased gradient estimates for discrete latent variable models},
  author={George Tucker and Andriy Mnih and Chris J. Maddison and John Lawson and Jascha Narain Sohl-Dickstein},
  journal={Neural Information Processing Systems (NIPS)},
  year={2017}
}
@article{Titsias2014DoublySV,
  title={Doubly Stochastic Variational {Bayes} for non-Conjugate Inference},
  author={Michalis K. Titsias and Miguel L{\'a}zaro-Gredilla},
  journal={International Conference on Machine Learning (ICML)},
  year={2014}
}

@article{Mnih2016VariationalIF,
  title={Variational Inference for {Monte Carlo} Objectives},
  author={Andriy Mnih and Danilo Jimenez Rezende},
  journal={International Conference on Machine Learning (ICML)},
  year={2016}
}
@article{Paisley2012VariationalBI,
  title={Variational {Bayesian} Inference with Stochastic Search},
  author={John William Paisley and David M. Blei and Michael I. Jordan},
  journal={International Conference on Machine Learning (ICML)},
  year={2012}
}
@article{Williams2004SimpleSG,
  title={Simple statistical gradient-following algorithms for connectionist reinforcement learning},
  author={Ronald J. Williams},
  journal={Machine Learning},
  year={2004},
  volume={8},
  pages={229-256}
}
@book{Bishop2007PatternRA,
  title={Pattern Recognition and Machine Learning},
  author={Bishop, Christopher M.},
  year={2006},
  publisher={Springer}
}
@article{Salimans2015MarkovCM,
  title={Markov Chain {Monte Carlo} and Variational Inference: Bridging the Gap},
  author={Tim Salimans and Diederik P. Kingma and Max Welling},
  journal={International Conference on Machine Learning (ICML)},
  year={2015}
}
@article{Yin2018SemiImplicitVI,
  title={Semi-Implicit Variational Inference},
  author={Mingzhang Yin and Mingyuan Zhou},
  journal={International Conference on Machine Learning (ICML)},
  year={2018}
}
@article{Hoffman2013StochasticVI,
  title={Stochastic variational inference},
  author={Matthew D. Hoffman and David M. Blei and Chong Wang and John William Paisley},
  journal={Journal of Machine Learning Research (JMLR)},
  year={2013},
  volume={14},
  pages={1303-1347}
}
@article{Child2021VeryDV,
  title={Very Deep {VAEs} Generalize Autoregressive Models and Can Outperform Them on Images},
  author={Rewon Child},
  journal={International Conference on Learning Representations (ICLR)},
  year={2021}
}
@article{Maale2019BIVAAV,
  title={{BIVA}: A Very Deep Hierarchy of Latent Variables for Generative Modeling},
  author={Lars Maal{\o}e and Marco Fraccaro and Valentin Li{\'e}vin and Ole Winther},
  journal={Neural Information Processing Systems (NeurIPS)},
  year={2019}
}

@article{Burda2016ImportanceWA,
  title={Importance Weighted Autoencoders},
  author={Yuri Burda and Roger Baker Grosse and Ruslan Salakhutdinov},
  journal={International Conference on Learning Representations (ICLR)},
  year={2016}
}
@article{JimenezRezende2014StochasticBA,
  title={Stochastic Backpropagation and Approximate Inference in Deep Generative Models},
  author={Danilo Jimenez Rezende and Shakir Mohamed and Daan Wierstra},
  journal={International Conference on Machine Learning (ICML)},
  year={2014}
}
@article{Kingma2014AutoEncodingVB,
  title={Auto-Encoding Variational {Bayes}},
  author={Diederik P. Kingma and Max Welling},
  journal={International Conference on Learning Representations (ICLR)},
  year={2014}
}
@article{Saul1996MeanFT,
  title={Mean Field Theory for Sigmoid Belief Networks},
  author={Lawrence K. Saul and T. Jaakkola and Michael I. Jordan},
  journal={Journal of Artificial Intelligence Research},
  year={1996},
  volume={4},
  pages={61-76}
}
@article{Jordan2004AnIT,
  title={An Introduction to Variational Methods for Graphical Models},
  author={Michael I. Jordan and Zoubin Ghahramani and Tommi Jaakkola and Lawrence K. Saul},
  journal={Machine Learning},
  year={2004},
  volume={37},
  pages={183-233}
}

@article{bengio2021foundations,
    title={{GFlowNet} Foundations},
    author={Yoshua Bengio and Salem Lahlou and Tristan Deleu and Edward Hu and Mo Tiwari and Emmanuel Bengio},
    journal={arXiv preprint 2111.09266},
    year={2021}
}
@article{pan2020softmax,
  title={Softmax deep double deterministic policy gradients},
  author={Pan, Ling and Cai, Qingpeng and Huang, Longbo},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={11767--11777},
  year={2020}
}
@article{mnih2015human,
  title={Human-level control through deep reinforcement learning},
  author={Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Rusu, Andrei A and Veness, Joel and Bellemare, Marc G and Graves, Alex and Riedmiller, Martin and Fidjeland, Andreas K and Ostrovski, Georg and others},
  journal={nature},
  volume={518},
  number={7540},
  pages={529--533},
  year={2015},
  publisher={Nature Publishing Group}
}
@inproceedings{fujimoto2018addressing,
  title={Addressing function approximation error in actor-critic methods},
  author={Fujimoto, Scott and Hoof, Herke and Meger, David},
  booktitle={International conference on machine learning},
  pages={1587--1596},
  year={2018},
  organization={PMLR}
}
@article{lillicrap2015continuous,
  title={Continuous control with deep reinforcement learning},
  author={Lillicrap, Timothy P and Hunt, Jonathan J and Pritzel, Alexander and Heess, Nicolas and Erez, Tom and Tassa, Yuval and Silver, David and Wierstra, Daan},
  journal={arXiv preprint arXiv:1509.02971},
  year={2015}
}
@article{jain2022multi,
  title={Multi-Objective GFlowNets},
  author={Jain, Moksh and Raparthy, Sharath Chandra and Hernandez-Garcia, Alex and Rector-Brooks, Jarrid and Bengio, Yoshua and Miret, Santiago and Bengio, Emmanuel},
  journal={arXiv preprint arXiv:2210.12765},
  year={2022}
}

@article{malkin2022trajectory,
  title={Trajectory Balance: Improved Credit Assignment in {GFlowNets}},
  author={Malkin, Nikolay and Jain, Moksh and Bengio, Emmanuel and Sun, Chen and Bengio, Yoshua},
  journal={Neural Information Processing Systems (NeurIPS)},
  year={2022}
}
@article{zhang2022generative,
title={Generative flow networks for discrete probabilistic modeling},
author={Zhang, Dinghuai and Malkin, Nikolay and Liu, Zhen and Volokhova, Alexandra and Courville, Aaron and Bengio, Yoshua},
journal={International Conference on Machine Learning (ICML)},
year={2022}
}

@article{jain2022biological,
title={Biological sequence design with {GFlowNets}},
author={Jain, Moksh and Bengio, Emmanuel and Hernandez-Garcia, Alex and Rector-Brooks, Jarrid and Dossou, Bonaventure F.P. and Ekbote, Chanakya and Fu, Jie and Zhang, Tianyu and Kilgour, Micheal and Zhang, Dinghuai and Simine, Lena and Das, Payel and Bengio, Yoshua},
journal={International Conference on Machine Learning (ICML)},
year={2022}
}

@article{deleu2022bayesian,
title={Bayesian Structure Learning with Generative Flow Networks},
author={Deleu, Tristan and G\'{o}is, Ant\'{o}nio and Emezue, Chris and Rankawat, Mansi and Lacoste-Julien, Simon and Bauer, Stefan and Bengio, Yoshua},
journal={Uncertainty in Artificial Intelligence (UAI)},
year={2022}
}

@article{mnih2014neural,
title={Neural Variational Inference and Learning in Belief Networks},
author={Mnih, Andriy and Gregor, Karol},
journal={International Conference on Machine Learning (ICML)},
year={2014}
}
@article{van2018deep,
      author={Hado van Hasselt and Yotam Doron and Florian Strub and Matteo Hessel and Nicolas Sonnerat and Joseph Modayil},
      title={Deep Reinforcement Learning and the Deadly Triad},
      year={2018},
      journal={arXiv preprint 1812.02648}
}

@article{bengio2020interference,
      title={Interference and generalization in temporal difference learning},
      author={Bengio, Emmanuel and Pineau, Joelle and Precup, Doina},
      journal={International Conference on Machine Learning (ICML)},
      year={2020}
}

@article{ilyas2020closer,
    title={A Closer Look at Deep Policy Gradients},
    author={Andrew Ilyas and Logan Engstrom and Shibani Santurkar and Dimitris Tsipras and Firdaus Janoos and Larry Rudolph and Aleksander Madry},
    journal={International Conference on Learning Representations (ICLR)},
    year={2020},
}

@article{Jin_2020,
   title={Chapter 11. Junction Tree Variational Autoencoder for Molecular Graph Generation},
   ISBN={9781788015479},
   ISSN={2041-3211},
   journal={Drug Discovery},
   publisher={Royal Society of Chemistry},
   author={Jin, Wengong and Barzilay, Regina and Jaakkola, Tommi},
   year={2020},
   pages={228–249}
}

@article{kumar2012fragment,
  title={Fragment based drug design: from experimental to computational approaches},
  author={Kumar, Ashutosh and Voet, Arnout and Zhang, Kam Y.J.},
  journal={Current medicinal chemistry},
  volume={19},
  number={30},
  pages={5128--5147},
  year={2012},
  publisher={Bentham Science Publishers}
}

@article{trott2010autodock,
      title={{AutoDock Vina}: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading},
      author={Trott, Oleg and Olson, Arthur J},
      journal={Journal of Computational Chemistry},
      volume={31},
      number={2},
      pages={455--461},
      year={2010},
      publisher={Wiley Online Library}
}
@article{kearns2000bias,
  title={Bias-Variance Error Bounds for Temporal Difference Updates.},
  author={Kearns, Michael J and Singh, Satinder P},
  journal={Conference on Learning Theory (COLT)},
  year={2000},
}

@article{sutton1988learning,
  title={Learning to predict by the methods of temporal differences},
  author={Sutton, Richard S},
  journal={Machine Learning},
  volume={3},
  number={1},
  pages={9--44},
  year={1988},
  publisher={Springer}
}

@article{baird1995residual,
  title={Residual algorithms: Reinforcement learning with function approximation},
  author={Baird, Leemon},
  journal={International Conference on Machine Learning (ICML)},
  year={1995}
}

@misc{gym_minigrid,
  author = {Chevalier-Boisvert, Maxime and Willems, Lucas},
  title = {Minimalistic Gridworld Environment for OpenAI Gym},
  year = {2018},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/maximecb/gym-minigrid}},
}

@article{chevalier2018babyai,   
title={BabyAI: First steps towards grounded language learning with a human in the loop},   
author={Chevalier-Boisvert, Maxime and Bahdanau, Dzmitry and Lahlou, Salem and Willems, Lucas and Saharia, Chitwan and Nguyen, Thien Huu and Bengio, Yoshua},   journal={International Conference on Learning Representations (ICLR)},   
year={2018} 
}

@article{schulman2017proximal,
  title={Proximal policy optimization algorithms},
  author={Schulman, John and Wolski, Filip and Dhariwal, Prafulla and Radford, Alec and Klimov, Oleg},
  journal={arXiv preprint 1707.06347},
  year={2017}
}
@article{haarnoja2018soft,
  title={Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor},
  author={Haarnoja, Tuomas and Zhou, Aurick and Abbeel, Pieter and Levine, Sergey},
  journal={International Conference on Machine Learning (ICML)},
  year={2018},
}
@article{haarnoja2017reinforcement,
  title={Reinforcement learning with deep energy-based policies},
  author={Haarnoja, Tuomas and Tang, Haoran and Abbeel, Pieter and Levine, Sergey},
  journal={International Conference on Machine Learning (ICML)},
  year={2017},
}
@article{nachum2017bridging,
  title={Bridging the gap between value and policy based reinforcement learning},
  author={Nachum, Ofir and Norouzi, Mohammad and Xu, Kelvin and Schuurmans, Dale},
  journal={Neural Information Processing Systems (NIPS)},
  year={2017}
}
@article{eysenbach2018diversity,
  title={Diversity is all you need: Learning skills without a reward function},
  author={Eysenbach, Benjamin and Gupta, Abhishek and Ibarz, Julian and Levine, Sergey},
  journal={International Conference on Learning Representations (ICLR)},
  year={2018}
}
@article{mnih2016asynchronous,
  title={Asynchronous methods for deep reinforcement learning},
  author={Mnih, Volodymyr and Badia, Adria Puigdomenech and Mirza, Mehdi and Graves, Alex and Lillicrap, Timothy and Harley, Tim and Silver, David and Kavukcuoglu, Koray},
journal={Neural Information Processing Systems (NIPS)},
  year={2016}
}
@article{zhang2021exploration,
  title={Exploration by maximizing R{\'e}nyi entropy for reward-free RL framework},
  author={Zhang, Chuheng and Cai, Yuanying and Huang, Longbo and Li, Jian},
  journal={Association for the Advancement of Artificial Intelligence (AAAI)},
  year={2021}
}
@article{islam2019marginalized,
  title={Marginalized state distribution entropy regularization in policy optimization},
  author={Islam, Riashat and Ahmed, Zafarali and Precup, Doina},
  journal={arXiv preprint 1912.05128},
  year={2019}
}
@article{hazan2019provably,
  title={Provably efficient maximum entropy exploration},
  author={Hazan, Elad and Kakade, Sham and Singh, Karan and Van Soest, Abby},
  journal={International Conference on Machine Learning (ICML)},
  year={2019},
}
@book{ziebart2010modeling,
  title={Modeling purposeful adaptive behavior with the principle of maximum causal entropy},
  author={Ziebart, Brian D},
  year={2010},
  publisher={Carnegie Mellon University}
}
@article{zhang2019deep,
  title={Deep residual reinforcement learning},
  author={Zhang, Shangtong and Boehmer, Wendelin and Whiteson, Shimon},
  journal={Autonomous Agents and Multi-Agent Systems (AAMAS)},
  year={2020}
}

@article{lake2018generalization,
  title={Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks},
  author={Lake, Brenden and Baroni, Marco},
  journal={International Conference on Machine Learning (ICML)},
  year={2018},
}

@article{bahdanau2018systematic,
  title={Systematic generalization: what is required and can it be learned?},
  author={Bahdanau, Dzmitry and Murty, Shikhar and Noukhovitch, Michael and Nguyen, Thien Huu and de Vries, Harm and Courville, Aaron},
  journal={International Conference on Learning Representations (ICLR)},
  year={2018}
}

@article{zimmermann2021nested,
  title={Nested Variational Inference},
  author={Heiko Zimmermann and Hao Wu and Babak Esmaeili and Sam Stites and  Jan-Willem van de Meent},
  journal={Neural Information Processing Systems (NeurIPS)},
  year={2021}
}

@article{ranganath2014black,
  title={Black box variational inference},
  author={Ranganath, Rajesh and Gerrish, Sean and Blei, David},
  journal={Artificial Intelligence and Statistics (AISTATS)},
  year={2014}
}

@article{ghahramani1999variational,
  title={Variational inference for {Bayesian} mixtures of factor analysers},
  author={Ghahramani, Zoubin and Beal, Matthew},
  journal={Neural Information Processing Systems (NIPS)},
  year={1999}
}

@article{ranganath2016hierarchical,
  title={Hierarchical variational models},
  author={Ranganath, Rajesh and Tran, Dustin and Blei, David},
  journal={International Conference on Machine Learning (ICML)},
  year={2016}
}

@article{vahdat2020nvae,
  title={NVAE: A deep hierarchical variational autoencoder},
  author={Vahdat, Arash and Kautz, Jan},
  journal={Neural Information Processing Systems (NeurIPS)},
  year={2020}
}

@article{sobolev2019importance,
  title={Importance weighted hierarchical variational inference},
  author={Sobolev, Artem and Vetrov, Dmitry},
  journal={Neural Information Processing Systems (NeurIPS)},
  year={2019}
}

@article{zhang2022unifying,
    title={Unifying generative models with {GFlowNets}},
    author={Zhang, Dinghuai and Chen, Ricky T. Q. and Malkin, Nikolay and Bengio, Yoshua},
    journal={arXiv preprint 2209.02606},
    year={2022}
}

@article{wan2020fdiv,
  author    = {Wan, Neng and Li, Dapeng and Hovakimyan, Naira},
  journal = {Neural Information Processing Systems (NeurIPS)},
  title     = {{f}-Divergence Variational Inference},
  year      = {2020}
}

@article{zhang2019variational,
  title   = {Variational f-divergence Minimization},
  author  = {Mingtian Zhang and Thomas Bird and Raza Habib and Tianlin Xu and David Barber},
  year    = {2019},
  journal = {arXiv preprint 1907.11891}
}

@article{wu2018variance,
  author    = {Cathy Wu and Aravind Rajeswaran and Yan Duan and Vikash Kumar and Alexandre M. Bayen and Sham M. Kakade and Igor Mordatch and Pieter Abbeel},
  title     = {Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines},
  journal = {International Conference on Learning Representations (ICLR)},
  year      = {2018}
}

@article{losh2017sgdr,
  author    = {Ilya Loshchilov and Frank Hutter},
  title     = {{SGDR:} Stochastic Gradient Descent with Warm Restarts},
  journal = {International Conference on Learning Representations (ICLR)},
  year      = {2017},
}

@article{bornschein2015rws,
  author    = {J{\"{o}}rg Bornschein and Yoshua Bengio},
  title     = {Reweighted Wake-Sleep},
  journal={International Conference on Learning Representations (ICLR)},
  year      = {2015}
}

@article{anh2019revisit,
  author    = {Tuan Anh Le and Adam R. Kosiorek and N. Siddharth and Yee Whye Teh and Frank Wood},
  title     = {Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow},
  journal = {Uncertainty in Artificial Intelligence (UAI)},
  year={2019}
}

@article{sonderby2016ladder,
 author = {S{\o}nderby, Casper Kaae and Raiko, Tapani and Maal{\o}e, Lars and S{\o}nderby, S{\o}ren Kaae and Winther, Ole},
 title = {Ladder variational autoencoders},
 journal = {Neural Information Processing Systems (NIPS)},
 year = {2016}
}

@article{blei2003hierarchical,
    author = {Blei, David M. and Jordan, Michael I. and Griffiths, Thomas L. and Tenenbaum, Joshua B.},
    title = {Hierarchical Topic Models and the Nested {Chinese} Restaurant Process},
    journal={Neural Information Processing Systems (NIPS)},
    year = {2003},
}

@article{williams1992simple,
  title={Simple statistical gradient-following algorithms for connectionist reinforcement learning},
  author={Williams, Ronald J},
  journal={Machine Learning},
  volume={8},
  number={3},
  pages={229--256},
  year={1992},
  publisher={Springer}
}


@article{madan2022learning,
    title={Learning {GFlowNets} from partial episodes for improved convergence and stability},
    author={Kanika Madan and Jarrid Rector-Brooks and Maksym Korablyov and Emmanuel Bengio and Moksh Jain and Andrei Nica and Tom Bosc and Yoshua Bengio and Nikolay Malkin},
    year={2022},
    journal={ICLR'2023; arXiv:2209.12782}
}

@article{weaver2001optimal,
title={The optimal reward baseline for gradient-based reinforcement learning},
author={Weaver, Lex and Tao, Nigel},
journal={Uncertainty in Artificial Intelligence (UAI)},
year={2001}
}

@article{battaglia2018gnn,
  title={Relational inductive biases, deep learning, and graph networks},
  author={Battaglia, Peter W and Hamrick, Jessica B and Bapst, Victor and Sanchez-Gonzalez, Alvaro and Zambaldi, Vinicius and Malinowski, Mateusz and Tacchetti, Andrea and Raposo, David and Santoro, Adam and Faulkner, Ryan and others},
  journal={arXiv preprint 1806.01261},
  year={2018}
}

@incollection{geiger1994bge,
  title={Learning {Gaussian} networks},
  author={Geiger, Dan and Heckerman, David},
  booktitle={Uncertainty Proceedings 1994},
  pages={235--243},
  year={1994},
  publisher={Elsevier}
}

@article{kuipers2014addendumbge,
  title={Addendum on the scoring of {Gaussian} directed acyclic graphical models},
  author={Kuipers, Jack and Moffa, Giusi and Heckerman, David},
  journal={The Annals of Statistics},
  volume={42},
  number={4},
  pages={1689--1691},
  year={2014},
  publisher={Institute of Mathematical Statistics}
}

@article{malkin2022gfnhvi,
  title={GFlowNets and variational inference},
  author={Malkin, Nikolay and Lahlou, Salem and Deleu, Tristan and Ji, Xu and Hu, Edward and Everett, Katie and Zhang, Dinghuai and Bengio, Yoshua},
  journal={arXiv preprint arXiv:2210.00580},
  year={2022}
}

@article{zimmermann2022variational,
  title={A variational perspective on generative flow networks},
  author={Zimmermann, Heiko and Lindsten, Fredrik and van de Meent, Jan-Willem and Naesseth, Christian A.},
  journal={arXiv preprint 2210.07992},
  year={2022}
}

@article{pan2022gafn,
  title={Generative Augmented Flow Networks},
  author={Ling Pan and Dinghuai Zhang and Aaron Courville and Longbo Huang and Yoshua Bengio},
  journal={arXiv preprint 2210.03308},
  year={2022}
}

@article{nishikawa2022bayesian,
  title={Bayesian learning of Causal Structure and Mechanisms with {GFlowNets} and Variational Bayes},
  author={Mizu Nishikawa-Toomey and Tristan Deleu and Jithendaraa Subramanian and Yoshua Bengio and Laurent Charlin},
  journal={arXiv preprint 2211.02763},
  year={2022}
}

@article{Pan2023BetterTO,
  title={Better Training of GFlowNets with Local Credit and Incomplete Trajectories},
  author={Ling Pan and Nikolay Malkin and Dinghuai Zhang and Yoshua Bengio},
  journal={ArXiv},
  year={2023},
  volume={abs/2302.01687}
}
@book{sutton2018reinforcement,
  title={Reinforcement learning: An introduction},
  author={Sutton, Richard S and Barto, Andrew G},
  year={2018},
  publisher={MIT press}
}

@article{yang2022dichotomy,
  title={Dichotomy of control: Separating what you can control from what you cannot},
  author={Yang, Mengjiao and Schuurmans, Dale and Abbeel, Pieter and Nachum, Ofir},
  journal={arXiv preprint arXiv:2210.13435},
  year={2022}
}

@article{machado2018revisiting,
  title={Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents},
  author={Machado, Marlos C and Bellemare, Marc G and Talvitie, Erik and Veness, Joel and Hausknecht, Matthew and Bowling, Michael},
  journal={Journal of Artificial Intelligence Research},
  volume={61},
  pages={523--562},
  year={2018}
}

@article{pirtskhalava2021dbaasp,
  title={DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics},
  author={Pirtskhalava, Malak and Amstrong, Anthony A and Grigolava, Maia and Chubinidze, Mindia and Alimbarashvili, Evgenia and Vishnepolsky, Boris and Gabrielian, Andrei and Rosenthal, Alex and Hurt, Darrell E and Tartakovsky, Michael},
  journal={Nucleic acids research},
  volume={49},
  number={D1},
  pages={D288--D297},
  year={2021},
  publisher={Oxford University Press}
}
@article{kunaver2017diversity,
  title={Diversity in recommender systems--A survey},
  author={Kunaver, Matev{\v{z}} and Po{\v{z}}rl, Toma{\v{z}}},
  journal={Knowledge-based systems},
  volume={123},
  pages={154--162},
  year={2017},
  publisher={Elsevier}
}
@inproceedings{zhang2020task,
  title={Task-oriented dialog systems that consider multiple appropriate responses under the same context},
  author={Zhang, Yichi and Ou, Zhijian and Yu, Zhou},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={05},
  pages={9604--9611},
  year={2020}
}
@article{robust-scheduling,
    title={Robust scheduling with {GFlowNets}},
    author={David Zhang and Corrado Rainone and Markus Peschl and Roberto Bondesan},
    journal={International Conference on Learning Representations (ICLR)},
    year={2023},
}
@inproceedings{antonoglou2021planning,
  title={Planning in stochastic environments with a learned model},
  author={Antonoglou, Ioannis and Schrittwieser, Julian and Ozair, Sherjil and Hubert, Thomas K and Silver, David},
  booktitle={International Conference on Learning Representations},
  year={2021}
}
@article{Liu2022GFlowOutDW,
  title={GFlowOut: Dropout with Generative Flow Networks},
  author={Dianbo Liu and Moksh Jain and Bonaventure F. P. Dossou and Qianli Shen and Salem Lahlou and Anirudh Goyal and Nikolay Malkin and Chris C. Emezue and Dinghuai Zhang and Nadhir Hassen and Xu Ji and Kenji Kawaguchi and Yoshua Bengio},
  journal={ArXiv},
  year={2022},
  volume={abs/2210.12928}
}
@article{bengio2021flow,
  title={Flow network based generative models for non-iterative diverse candidate generation},
  author={Bengio, Emmanuel and Jain, Moksh and Korablyov, Maksym and Precup, Doina and Bengio, Yoshua},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={27381--27394},
  year={2021}
}
@article{xie2021mars,
  title={Mars: Markov molecular sampling for multi-objective drug discovery},
  author={Xie, Yutong and Shi, Chence and Zhou, Hao and Yang, Yuwei and Zhang, Weinan and Yu, Yong and Li, Lei},
  journal={arXiv preprint arXiv:2103.10432},
  year={2021}
}

@article{song2021large,
  title={Large-scale de novo oligonucleotide synthesis for whole-genome synthesis and data storage: Challenges and opportunities},
  author={Song, Li-Fu and Deng, Zheng-Hua and Gong, Zi-Yi and Li, Lu-Lu and Li, Bing-Zhi},
  journal={Frontiers in bioengineering and biotechnology},
  volume={9},
  pages={689797},
  year={2021},
  publisher={Frontiers Media SA}
}
@inproceedings{trabucco2022design,
  title={Design-bench: Benchmarks for data-driven offline model-based optimization},
  author={Trabucco, Brandon and Geng, Xinyang and Kumar, Aviral and Levine, Sergey},
  booktitle={International Conference on Machine Learning},
  pages={21658--21676},
  year={2022},
  organization={PMLR}
}
