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

arXiv: https://arxiv.org/abs/2012.13169 · HTML: https://mlsanity.com/paper/2012.13169/

## Key facts

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

## Built on by (most cited first, 22 of 22)

- [Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach](https://mlsanity.com/paper/2312.11865/) (2023)
- [Counter-Strike Deathmatch with Large-Scale Behavioural Cloning](https://mlsanity.com/paper/2104.04258/) (2021)
- [A Survey on Self-play Methods in Reinforcement Learning](https://mlsanity.com/paper/2408.01072/) (2024)
- [On Efficient Reinforcement Learning for Full-length Game of StarCraft II](https://mlsanity.com/paper/2209.11553/) (2022)
- [SMAC-R1: The Emergence of Intelligence in Decision-Making Tasks](https://mlsanity.com/paper/2410.16024/) (2024)
- [Distributed Deep Reinforcement Learning: A Survey and A Multi-Player Multi-Agent Learning Toolbox](https://mlsanity.com/paper/2212.00253/) (2022)
- [Best Practice Critic Optimization](https://mlsanity.com/paper/2608.23566/) (2026)
- [On games and simulators as a platform for development of artificial intelligence for command and control](https://mlsanity.com/paper/2110.11305/) (2021)
- [SC2EGSet: StarCraft II Esport Replay and Game-state Dataset](https://mlsanity.com/paper/2207.03428/) (2022)
- [Revisiting Experience Replayable Conditions](https://mlsanity.com/paper/2402.10374/) (2024)
- [TacticCraft: Natural Language-Driven Tactical Adaptation for StarCraft II](https://mlsanity.com/paper/2507.15618/) (2025)
- [Rethinking of AlphaStar](https://mlsanity.com/paper/2108.03452/) (2021)
- [AI in Human-computer Gaming: Techniques, Challenges and Opportunities](https://mlsanity.com/paper/2111.07631/) (2021)
- [Solving a Real-World Optimization Problem Using Proximal Policy Optimization with Curriculum Learning and Reward Engineering](https://mlsanity.com/paper/2404.02577/) (2024)
- [Reinforcement Learning via Conservative Agent for Environments with Random Delays](https://mlsanity.com/paper/2507.18992/) (2025)
- [In-Context Compositional Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2509.24067/) (2025)
- [Applying supervised and reinforcement learning methods to create neural-network-based agents for playing StarCraft II](https://mlsanity.com/paper/2109.12691/) (2021)
- [Prioritized League Reinforcement Learning for Large-Scale Heterogeneous Multiagent Systems](https://mlsanity.com/paper/2403.18057/) (2024)
- [A Framework for testing Federated Learning algorithms using an edge-like environment](https://mlsanity.com/paper/2407.12980/) (2024)
- [Curriculum RL meets Monte Carlo Planning: Optimization of a Real World Container Management Problem](https://mlsanity.com/paper/2503.17194/) (2025)
- [Two-Bridge: Exclusive Objectives and Extended Horizon StarCraft II Benchmark](https://mlsanity.com/paper/2603.06608/) (2026)
- [NePPO: Near-Potential Policy Optimization for General-Sum Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2603.06977/) (2026)

## 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.