mlsanityData to 14 Oct 2026

Artificial Generals Intelligence: Mastering Generals.io with Reinforcement Learning

arXiv 2507.06825 ↗First posted 9 Jul 2025cs.LG cs.AIcited by 1

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

We introduce a real-time strategy game environment based on Generals.io, a game with thousands of weekly active players. Our environment is fully compatible with Gymnasium and PettingZoo and is capable of running thousands of frames per second on commodity hardware. We also present a reference agent, trained with supervised pre-training and self-play, which reached the top 0.003% of the 1v1 human leaderboard after only 36 hours on a single H100 GPU. To accelerate learning, we incorporate potential-based reward shaping and memory features. Our contributions of a modular RTS benchmark and a competitive baseline agent provide an accessible yet challenging platform for advancing multi-agent reinforcement learning research. The documented code, together with examples and tutorials, is available at https://github.com/strakam/generals-bots.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 17 references that are in the map. First posted on arXiv 9 Jul 2025.

Builds on

10 of 17 references.

References · 10

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  3. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  4. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,103
  5. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 734
  6. 2019The StarCraft Multi-Agent Challengecited by 597
  7. 2019Emergent Tool Use From Multi-Agent Autocurriculacited by 313
  8. 2017DeepStack: Expert-Level Artificial Intelligence in No-Limit Pokercited by 233
  9. 2022Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learningcited by 131
  10. 2024FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learningcited by 13

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Superhuman AI for Generals.io Using Self-Play Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Learning Game-Playing Agents with Generative Code Optimization
  2. 2024Massively Multiagent Minigames for Training Generalist Agents
  3. 2026Can AI Agents Learn Their Way to the Top? Evaluating Heuristic Learning in a Long-Running Game Agent Competition
  4. 2025VGC-Bench: Towards Mastering Diverse Team Strategies in Competitive Pok'emon
  5. 2024PokeLLMon: A Human-Parity Agent for Pokemon Battles with Large Language Models
  6. 2026AI Gamestore: Scalable, Open-Ended Evaluation of Machine General Intelligence with Human Games
  7. 2024Reinforcing Competitive Multi-Agents for Playing 'So Long Sucker'
  8. 2025Human-Level Competitive Pok'emon via Scalable Offline Reinforcement Learning with Transformers