mlsanityData to 14 Oct 2026

SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II

arXiv 2012.13169 ↗First posted 24 Dec 2020cs.LGcited by 22

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Abstract (arXiv)

AlphaStar, the AI that reaches GrandMaster level in StarCraft II, is a remarkable milestone demonstrating what deep reinforcement learning can achieve in complex Real-Time Strategy (RTS) games. However, the complexities of the game, algorithms and systems, and especially the tremendous amount of computation needed are big obstacles for the community to conduct further research in this direction. We propose a deep reinforcement learning agent, StarCraft Commander (SCC). With order of magnitude less computation, it demonstrates top human performance defeating GrandMaster players in test matches and top professional players in a live event. Moreover, it shows strong robustness to various human strategies and discovers novel strategies unseen from human plays. In this paper, we will share the key insights and optimizations on efficient imitation learning and reinforcement learning for StarCraft II full game.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Dec 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 3; 2023: 1; 2024: 6; 2025: 4; 2026: 3.

22 papers.

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  1. 2023Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approachcited by 79
  2. 2021Counter-Strike Deathmatch with Large-Scale Behavioural Cloningcited by 50
  3. 2024A Survey on Self-play Methods in Reinforcement Learningcited by 26
  4. 2022On Efficient Reinforcement Learning for Full-length Game of StarCraft IIcited by 14
  5. 2024SMAC-R1: The Emergence of Intelligence in Decision-Making Taskscited by 6
  6. 2022Distributed Deep Reinforcement Learning: A Survey and A Multi-Player Multi-Agent Learning Toolboxcited by 4
  7. 2026Best Practice Critic Optimizationcited by 4
  8. 2021On games and simulators as a platform for development of artificial intelligence for command and controlcited by 3
  9. 2022SC2EGSet: StarCraft II Esport Replay and Game-state Datasetcited by 3
  10. 2024Revisiting Experience Replayable Conditionscited by 3
  11. 2025TacticCraft: Natural Language-Driven Tactical Adaptation for StarCraft IIcited by 2
  12. 2021Rethinking of AlphaStarcited by 1
10 more
  1. 2021AI in Human-computer Gaming: Techniques, Challenges and Opportunitiescited by 1
  2. 2024Solving a Real-World Optimization Problem Using Proximal Policy Optimization with Curriculum Learning and Reward Engineeringcited by 1
  3. 2025Reinforcement Learning via Conservative Agent for Environments with Random Delayscited by 1
  4. 2025In-Context Compositional Q-Learning for Offline Reinforcement Learningcited by 1
  5. 2021Applying supervised and reinforcement learning methods to create neural-network-based agents for playing StarCraft IIcited by 0
  6. 2024Prioritized League Reinforcement Learning for Large-Scale Heterogeneous Multiagent Systemscited by 0
  7. 2024A Framework for testing Federated Learning algorithms using an edge-like environmentcited by 0
  8. 2025Curriculum RL meets Monte Carlo Planning: Optimization of a Real World Container Management Problemcited by 0
  9. 2026Two-Bridge: Exclusive Objectives and Extended Horizon StarCraft II Benchmarkcited by 0
  10. 2026NePPO: Near-Potential Policy Optimization for General-Sum Multi-Agent Reinforcement Learningcited by 0

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  1. 2020TStarBot-X: An Open-Sourced and Comprehensive Study for Efficient League Training in StarCraft II Full Game
  2. 2023AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning
  3. 2025StarCraft+: Benchmarking Multi-agent Algorithms in Adversary Paradigm
  4. 2025HLSMAC: A New StarCraft Multi-Agent Challenge for High-Level Strategic Decision-Making
  5. 2025Society of Mind Meets Real-Time Strategy: A Hierarchical Multi-Agent Framework for Strategic Reasoning
  6. 2026World Models for Policy Refinement in StarCraft II
  7. 2025AVA: Attentive VLM Agent for Mastering StarCraft II
  8. 2025A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games