mlsanityData to 7 Oct 2026

Expected flow networks in stochastic environments and two-player zero-sum games

arXiv 2310.02779 ↗First posted 4 Oct 2023cs.LG cs.GTcited by 7

Not on the map. Why?

Abstract (arXiv)

Generative flow networks (GFlowNets) are sequential sampling models trained to match a given distribution. GFlowNets have been successfully applied to various structured object generation tasks, sampling a diverse set of high-reward objects quickly. We propose expected flow networks (EFlowNets), which extend GFlowNets to stochastic environments. We show that EFlowNets outperform other GFlowNet formulations in stochastic tasks such as protein design. We then extend the concept of EFlowNets to adversarial environments, proposing adversarial flow networks (AFlowNets) for two-player zero-sum games. We show that AFlowNets learn to find above 80% of optimal moves in Connect-4 via self-play and outperform AlphaZero in tournaments.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 26 of its 47 references that are in the map. First posted on arXiv 4 Oct 2023.

Builds on

26 of 47 references.

References · 26

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  2. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,327
  3. 2017A Distributional Perspective on Reinforcement Learningcited by 743
  4. 2017Reinforcement Learning with Deep Energy-Based Policiescited by 659
  5. 2021Flow Network based Generative Models for Non-Iterative Diverse Candidate Generationcited by 384
  6. 2021GFlowNet Foundationscited by 286
  7. 2022Trajectory balance: Improved credit assignment in GFlowNetscited by 244
  8. 2019A Theoretical Analysis of Deep Q-Learningcited by 212
  9. 2017Bridging the Gap Between Value and Policy Based Reinforcement Learningcited by 178
  10. 2022Biological Sequence Design with GFlowNetscited by 172
  11. 2022Bayesian Structure Learning with Generative Flow Networkscited by 153
  12. 2022Learning GFlowNets from partial episodes for improved convergence and stabilitycited by 129
14 more
  1. 2022Generative Flow Networks for Discrete Probabilistic Modelingcited by 113
  2. 2022GFlowNets and variational inferencecited by 103
  3. 2023A theory of continuous generative flow networkscited by 98
  4. 2023Robust Scheduling with GFlowNetscited by 67
  5. 2022A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inferencecited by 50
  6. 2023Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Networkcited by 50
  7. 2022Dichotomy of Control: Separating What You Can Control from What You Cannotcited by 47
  8. 2023GFlowNet-EM for learning compositional latent variable modelscited by 40
  9. 2022A Variational Perspective on Generative Flow Networkscited by 39
  10. 2023Stochastic Generative Flow Networkscited by 32
  11. 2023Thompson sampling for improved exploration in GFlowNetscited by 31
  12. 2023Distributional GFlowNets with Quantile Flowscited by 28
  13. 2023DAG Matters! GFlowNets Enhanced Explainer For Graph Neural Networkscited by 27
  14. 2022GFlowOut: Dropout with Generative Flow Networkscited by 22

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 3; 2026: 2.

7 papers.

  1. 2024Discrete Probabilistic Inference as Control in Multi-path Environmentscited by 40
  2. 2023Maximum entropy GFlowNets with soft Q-learningcited by 30
  3. 2025Generative Models in Decision Making: A Surveycited by 14
  4. 2025Loss-Guided Auxiliary Agents for Overcoming Mode Collapse in GFlowNetscited by 4
  5. 2025Discrete Compositional Generation via General Soft Operators and Robust Reinforcement Learningcited by 2
  6. 2026WINFlowNets: Warm-up Integrated Networks Training of Generative Flow Networks for Robotics and Machine Fault Adaptationcited by 0
  7. 2026IFlowNets: Extending Generative Samplers to Learn Strategies in Incomplete Information Gamescited by 0