Starcraft II
81 papers with code • 3 benchmarks • 4 datasets
Starcraft II is a RTS game; the task is to train an agent to play the game.
( Image credit: The StarCraft Multi-Agent Challenge )
Benchmarks
These leaderboards are used to track progress in Starcraft II
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Libraries
Use these libraries to find Starcraft II models and implementations
StarCraft II Learning Environment
SMAC-Exp
Lani
MSC
Most implemented papers
MSC: A Dataset for Macro-Management in StarCraft II
wuhuikai/MSC
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We also split MSC into training, validation and test set for the convenience of evaluation and comparison.
Explainable Reinforcement Learning Through a Causal Lens
prashanm/StarCraft-II-causal-explanations
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In this paper, we use causal models to derive causal explanations of behaviour of reinforcement learning agents.
Arena: a toolkit for Multi-Agent Reinforcement Learning
We introduce Arena, a toolkit for multi-agent reinforcement learning (MARL) research.
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control
saizhang0218/VBC
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NeurIPS 2019
Multi-agent reinforcement learning (MARL) has recently received considerable attention due to its applicability to a wide range of real-world applications.
Deep Coordination Graphs
wendelinboehmer/dcg
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ICML 2020
This paper introduces the deep coordination graph (DCG) for collaborative multi-agent reinforcement learning.
RODE: Learning Roles to Decompose Multi-Agent Tasks
TonghanWang/RODE
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ICLR 2021
Learning a role selector based on action effects makes role discovery much easier because it forms a bi-level learning hierarchy -- the role selector searches in a smaller role space and at a lower temporal resolution, while role policies learn in significantly reduced primitive action-observation spaces.
Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement Learning
hijkzzz/pymarl2
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Multi-Agent Reinforcement Learning (MARL) has seen revolutionary breakthroughs with its successful application to multi-agent cooperative tasks such as computer games and robot swarms.
Celebrating Diversity in Shared Multi-Agent Reinforcement Learning
Recently, deep multi-agent reinforcement learning (MARL) has shown the promise to solve complex cooperative tasks.
Rethinking of AlphaStar
liuruoze/Raw-vs-Human-in-AlphaStar
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After the discussion, we present the future research directions for these problems.
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven Exploration
hyunghona/emu
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NeurIPS 2021
Efficient exploration in deep cooperative multi-agent reinforcement learning (MARL) still remains challenging in complex coordination problems.
