Starcraft
129 papers with code • 0 benchmarks • 6 datasets
Starcraft I is a RTS game; the task is to train an agent to play the game.
( Image credit: Macro Action Selection with Deep Reinforcement Learning in StarCraft )
Benchmarks
These leaderboards are used to track progress in Starcraft
Libraries
Use these libraries to find Starcraft models and implementations
VizDoom
StarCraft II Learning Environment
Mario AI
Lani
StarData
MSC
Latest papers
Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning
Wei9711/GACG
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To overcome these limitations, we present a novel approach to infer the Group-Aware Coordination Graph (GACG), which is designed to capture both the cooperation between agent pairs based on current observations and group-level dependencies from behaviour patterns observed across trajectories.
Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning
Wei9711/LTSCG
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The LTS-CG leverages agents' historical observations to calculate an agent-pair probability matrix, where a sparse graph is sampled from and used for knowledge exchange between agents, thereby simultaneously capturing agent dependencies and relation uncertainty.
PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement Learning
Agents share Q-value network periodically during the training process.
Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement Learning
hyunghona/emu
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To address this, we introduce Efficient episodic Memory Utilization (EMU) for MARL, with two primary objectives: (a) accelerating reinforcement learning by leveraging semantically coherent memory from an episodic buffer and (b) selectively promoting desirable transitions to prevent local convergence.
SwarmBrain: Embodied agent for real-time strategy game StarCraft II via large language models
In this paper, we introduce SwarmBrain, an embodied agent leveraging LLM for real-time strategy implementation in the StarCraft II game environment.
Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach
histmeisah/large-language-models-play-starcraftii
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StarCraft II is a challenging benchmark for AI agents due to the necessity of both precise micro level operations and strategic macro awareness.
CODEX: A Cluster-Based Method for Explainable Reinforcement Learning
Despite the impressive feats demonstrated by Reinforcement Learning (RL), these algorithms have seen little adoption in high-risk, real-world applications due to current difficulties in explaining RL agent actions and building user trust.
JaxMARL: Multi-Agent RL Environments in JAX
flairox/jaxmarl
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This not only enables GPU acceleration, but also provides a more flexible MARL environment, unlocking the potential for self-play, meta-learning, and other future applications in MARL.
FoX: Formation-aware exploration in multi-agent reinforcement learning
hyeon1996/fox
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Recently, deep multi-agent reinforcement learning (MARL) has gained significant popularity due to its success in various cooperative multi-agent tasks.
AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning
StarCraft II is one of the most challenging simulated reinforcement learning environments; it is partially observable, stochastic, multi-agent, and mastering StarCraft II requires strategic planning over long time horizons with real-time low-level execution.
