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 with no code
N-Agent Ad Hoc Teamwork
POAM is a policy gradient, multi-agent reinforcement learning approach to the NAHT problem, that enables adaptation to diverse teammate behaviors by learning representations of teammate behaviors.
Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaboration
Second, we introduce a heterogeneous layer for decision-making, whose parameters are specifically generated by the learned latent variables.
MARL-LNS: Cooperative Multi-agent Reinforcement Learning via Large Neighborhoods Search
Cooperative multi-agent reinforcement learning (MARL) has been an increasingly important research topic in the last half-decade because of its great potential for real-world applications.
Collaborative AI Teaming in Unknown Environments via Active Goal Deduction
With the advancements of artificial intelligence (AI), we're seeing more scenarios that require AI to work closely with other agents, whose goals and strategies might not be known beforehand.
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning
Multi-Agent Reinforcement Learning (MARL) algorithms face the challenge of efficient exploration due to the exponential increase in the size of the joint state-action space.
SMAUG: A Sliding Multidimensional Task Window-Based MARL Framework for Adaptive Real-Time Subtask Recognition
Instead of making behavioral decisions directly from the exponentially expanding joint observational-action space, subtask-based multi-agent reinforcement learning (MARL) methods enable agents to learn how to tackle different subtasks.
Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning
To address this limitation, we propose Imagine, Initialize, and Explore (IIE), a novel method that offers a promising solution for efficient multi-agent exploration in complex scenarios.
Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation
Multi-Agent Reinforcement Learning (MARL) algorithms are widely adopted in tackling complex tasks that require collaboration and competition among agents in dynamic Multi-Agent Systems (MAS).
MAIDCRL: Semi-centralized Multi-Agent Influence Dense-CNN Reinforcement Learning
Distributed decision-making in multi-agent systems presents difficult challenges for interactive behavior learning in both cooperative and competitive systems.
COA-GPT: Generative Pre-trained Transformers for Accelerated Course of Action Development in Military Operations
The development of Courses of Action (COAs) in military operations is traditionally a time-consuming and intricate process.
