mlsanityData to 7 Oct 2026

Cogment: Open Source Framework For Distributed Multi-actor Training, Deployment & Operations

arXiv 2106.11345 ↗First posted 21 Jun 2021cs.AI cs.HC cs.LGcited by 3

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Abstract (arXiv)

Involving humans directly for the benefit of AI agents' training is getting traction thanks to several advances in reinforcement learning and human-in-the-loop learning. Humans can provide rewards to the agent, demonstrate tasks, design a curriculum, or act in the environment, but these benefits also come with architectural, functional design and engineering complexities. We present Cogment, a unifying open-source framework that introduces an actor formalism to support a variety of humans-agents collaboration typologies and training approaches. It is also scalable out of the box thanks to a distributed micro service architecture, and offers solutions to the aforementioned complexities.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 46 of its 71 references that are in the map. First posted on arXiv 21 Jun 2021.

Builds on

46 of 71 references.

References · 46

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2017Deep reinforcement learning from human preferencescited by 3,846
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  4. 2020Learning to summarize from human feedbackcited by 2,562
  5. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  6. 2016Generative Adversarial Imitation Learningcited by 1,576
  7. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  8. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,084
  9. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 723
  10. 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 516
  11. 2018An Algorithmic Perspective on Imitation Learningcited by 455
  12. 2018Scalable agent alignment via reward modeling: a research directioncited by 438
34 more
  1. 2018Reward learning from human preferences and demonstrations in Ataricited by 314
  2. 2019Emergent Tool Use From Multi-Agent Autocurriculacited by 308
  3. 2019Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialogcited by 283
  4. 2018Learning to Drive in a Daycited by 266
  5. 2017Thinking Fast and Slow with Deep Learning and Tree Searchcited by 245
  6. 2021Offline Reinforcement Learning with Fisher Divergence Critic Regularizationcited by 237
  7. 2019The Hanabi Challenge: A New Frontier for AI Researchcited by 194
  8. 2019Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutionscited by 182
  9. 2020Acme: A Research Framework for Distributed Reinforcement Learningcited by 146
  10. 2017Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Playcited by 139
  11. 2020Primal Wasserstein Imitation Learningcited by 120
  12. 2019Learning from Dialogue after Deployment: Feed Yourself, Chatbot!cited by 102
  13. 2020Sample Efficient Reinforcement Learning with REINFORCEcited by 85
  14. 2019Better Rewards Yield Better Summaries: Learning to Summarise Without Referencescited by 76
  15. 2018Can Neural Machine Translation be Improved with User Feedback?cited by 71
  16. 2021Asymmetric self-play for automatic goal discovery in robotic manipulationcited by 71
  17. 2018Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learningcited by 67
  18. 2019Learning Reward Functions by Integrating Human Demonstrations and Preferencescited by 64
  19. 2020Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learningcited by 57
  20. 2017RLlib: Abstractions for Distributed Reinforcement Learningcited by 53
  21. 2019Simplified Action Decoder for Deep Multi-Agent Reinforcement Learningcited by 47
  22. 2019Leveraging Human Guidance for Deep Reinforcement Learning Taskscited by 42
  23. 2019rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorchcited by 38
  24. 2019Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluatorscited by 34
  25. 2018Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedbackcited by 32
  26. 2021Continuous Coordination As a Realistic Scenario for Lifelong Learningcited by 31
  27. 2021Reverb: A Framework For Experience Replaycited by 27
  28. 2018Active Neural Localizationcited by 18
  29. 2020DeepAveragers: Offline Reinforcement Learning by Solving Derived Non-Parametric MDPscited by 16
  30. 2021Theory of Mind for Deep Reinforcement Learning in Hanabicited by 11
  31. 2017Evaluating and Modelling Hanabi-Playing Agentscited by 9
  32. 2018Genetic-Gated Networks for Deep Reinforcementcited by 7
  33. 2019Deep Active Localizationcited by 6
  34. 2020Human AI interaction loop training: New approach for interactive reinforcement learningcited by 2

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Cited by year

Citing papers per year: 2023: 2; 2024: 0; 2025: 1.

3 papers.

  1. 2023Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learningcited by 2
  2. 2023Human-Machine Teaming for UAVs: An Experimentation Platformcited by 0
  3. 2025A Systematic Approach to Design Real-World Human-in-the-Loop Deep Reinforcement Learning: Salient Features, Challenges and Trade-offscited by 0