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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

Showing 201225 of 311 papers

TitleStatusHype
DOP: Off-Policy Multi-Agent Decomposed Policy Gradients0
Coordinated Multi-Agent Exploration Using Shared Goals0
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms0
FSV: Learning to Factorize Soft Value Function for Cooperative Multi-Agent Reinforcement Learning0
Factored Action Spaces in Deep Reinforcement Learning0
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II0
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 Learning0
Reinforcement Learning for the Beginning of Starcraft II Game0
Multi-agent Policy Optimization with Approximatively Synchronous Advantage Estimation0
TStarBot-X: An Open-Sourced and Comprehensive Study for Efficient League Training in StarCraft II Full GameCode1
TLeague: A Framework for Competitive Self-Play based Distributed Multi-Agent Reinforcement LearningCode1
Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?Code1
Multi-Agent Collaboration via Reward Attribution DecompositionCode1
Graph Convolutional Value Decomposition in Multi-Agent Reinforcement LearningCode1
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning0
RODE: Learning Roles to Decompose Multi-Agent TasksCode1
Towards Understanding Linear Value Decomposition in Cooperative Multi-Agent Q-Learning0
AI and Wargaming0
Energy-based Surprise Minimization for Multi-Agent Value FactorizationCode1
QR-MIX: Distributional Value Function Factorisation for Cooperative Multi-Agent Reinforcement Learning0
BGC: Multi-Agent Group Belief with Graph Clustering0
Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection0
QPLEX: Duplex Dueling Multi-Agent Q-LearningCode1
Improving Multi-Agent Cooperation using Theory of Mind0
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