mlsanityData to 14 Oct 2026

Replication of Multi-agent Reinforcement Learning for the "Hide and Seek" Problem

arXiv 2310.05430 ↗First posted 9 Oct 2023cs.AI cs.LG cs.MAcited by 0

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Abstract (arXiv)

Reinforcement learning generates policies based on reward functions and hyperparameters. Slight changes in these can significantly affect results. The lack of documentation and reproducibility in Reinforcement learning research makes it difficult to replicate once-deduced strategies. While previous research has identified strategies using grounded maneuvers, there is limited work in more complex environments. The agents in this study are simulated similarly to Open Al's hider and seek agents, in addition to a flying mechanism, enhancing their mobility, and expanding their range of possible actions and strategies. This added functionality improves the Hider agents to develop a chasing strategy from approximately 2 million steps to 1.6 million steps and hiders

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 47 of its 101 references that are in the map. First posted on arXiv 9 Oct 2023.

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47 of 101 references.

References · 47

  1. 2016Layer Normalizationcited by 5,669
  2. 2016Concrete Problems in AI Safetycited by 1,642
  3. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  4. 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,194
  5. 2017Deep Reinforcement Learning that Matterscited by 754
  6. 2017Counterfactual Multi-Agent Policy Gradientscited by 751
  7. 2017Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrationscited by 715
  8. 2018Learning Dexterous In-Hand Manipulationcited by 687
  9. 2016Unifying Count-Based Exploration and Intrinsic Motivationcited by 639
  10. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 575
  11. 2017Population Based Training of Neural Networkscited by 367
  12. 2016Learning Multiagent Communication with Backpropagationcited by 366
35 more
  1. 2019Emergent Tool Use From Multi-Agent Autocurriculacited by 313
  2. 2018Large-Scale Study of Curiosity-Driven Learningcited by 280
  3. 2018Human-level performance in first-person multiplayer games with population-based deep reinforcement learningcited by 264
  4. 2017Emergence of Grounded Compositional Language in Multi-Agent Populationscited by 263
  5. 2017Count-Based Exploration with Neural Density Modelscited by 257
  6. 2017Asymmetric Actor Critic for Image-Based Robot Learningcited by 238
  7. 2017One-Shot Imitation Learningcited by 233
  8. 2017Multi-agent Reinforcement Learning in Sequential Social Dilemmascited by 209
  9. 2017Reverse Curriculum Generation for Reinforcement Learningcited by 198
  10. 2019Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutionscited by 192
  11. 2017Emergence of Locomotion Behaviours in Rich Environmentscited by 165
  12. 2015Variational Information Maximisation for Intrinsically Motivated Reinforcement Learningcited by 144
  13. 2017Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Playcited by 141
  14. 2018Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applicationscited by 127
  15. 2017Emergent Complexity via Multi-Agent Competitioncited by 120
  16. 2017Surprise-Based Intrinsic Motivation for Deep Reinforcement Learningcited by 83
  17. 2017Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learningcited by 83
  18. 2018The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communitiescited by 78
  19. 2017A multi-agent reinforcement learning model of common-pool resource appropriationcited by 69
  20. 2022EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Enginecited by 62
  21. 2019Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Researchcited by 61
  22. 2019Emergent Coordination Through Competitioncited by 57
  23. 2019Structured agents for physical constructioncited by 50
  24. 2018Learning to Play with Intrinsically-Motivated Self-Aware Agentscited by 35
  25. 2017Deep Learning for Video Game Playingcited by 30
  26. 2020Deep Reinforcement Learning for Navigation in AAA Video Gamescited by 27
  27. 2018Inverse reinforcement learning for video gamescited by 26
  28. 2020Decentralized Multi-Agent Pursuit using Deep Reinforcement Learningcited by 23
  29. 2020Exploration-Exploitation in Multi-Agent Learning: Catastrophe Theory Meets Game Theorycited by 17
  30. 2018Malthusian Reinforcement Learningcited by 14
  31. 2021On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learningcited by 10
  32. 2019Attention-based Curiosity-driven Exploration in Deep Reinforcement Learningcited by 5
  33. 2020Replication Markets: Results, Lessons, Challenges and Opportunities in AI Replicationcited by 5
  34. 2022Obtaining Robust Control and Navigation Policies for Multi-Robot Navigation via Deep Reinforcement Learningcited by 3
  35. 2022Retro-RL: Reinforcing Nominal Controller With Deep Reinforcement Learning for Tilting-Rotor Dronescited by 2

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