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Starcraft

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 )

Papers

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Showing 226–250 of 311 papers

TitleStatusHype
Coordinated Multi-Agent Exploration Using Shared Goals—0
FSV: Learning to Factorize Soft Value Function for Cooperative Multi-Agent Reinforcement Learning—0
DOP: Off-Policy Multi-Agent Decomposed Policy Gradients—0
RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning—0
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms—0
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II—0
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement LearningCode0
Exact Reduction of Huge Action Spaces in General Reinforcement Learning—0
Reinforcement Learning for the Beginning of Starcraft II Game—0
Multi-agent Policy Optimization with Approximatively Synchronous Advantage Estimation—0
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning—0
Towards Understanding Linear Value Decomposition in Cooperative Multi-Agent Q-Learning—0
AI and Wargaming—0
QR-MIX: Distributional Value Function Factorisation for Cooperative Multi-Agent Reinforcement Learning—0
BGC: Multi-Agent Group Belief with Graph Clustering—0
Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection—0
Improving Multi-Agent Cooperation using Theory of Mind—0
Artificial Intelligence is stupid and causal reasoning won't fix it—0
QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning—0
Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition and Selective Transfer—0
StarCraft II Build Order Optimization using Deep Reinforcement Learning and Monte-Carlo Tree Search—0
Incorporating Pragmatic Reasoning Communication into Emergent Language—0
Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization—0
The Adversarial Resilience Learning Architecture for AI-based Modelling, Exploration, and Operation of Complex Cyber-Physical Systems—0
Optimal Any-Angle Pathfinding on a Sphere—0
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