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Starcraft II

Starcraft II is a RTS game; the task is to train an agent to play the game.

( Image credit: The StarCraft Multi-Agent Challenge )

Papers

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Showing 126–150 of 175 papers

TitleStatusHype
Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment—0
Multi-Agent Deep Reinforcement Learning using Attentive Graph Neural Architectures for Real-Time Strategy Games—0
SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning—0
Deep Convolution for Irregularly Sampled Temporal Point Clouds—0
NQMIX: Non-monotonic Value Function Factorization for Deep Multi-Agent Reinforcement Learning—0
Regularized Softmax Deep Multi-Agent Q-Learning—0
Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning—0
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents—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
Spatially Structured Recurrent Modules—0
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II—0
Reinforcement Learning for the Beginning of Starcraft II Game—0
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning—0
Towards Understanding Linear Value Decomposition in Cooperative Multi-Agent Q-Learning—0
Spatially Structured Recurrent Modules—0
BGC: Multi-Agent Group Belief with Graph Clustering—0
Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection—0
S2RMs: Spatially Structured Recurrent Modules—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
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning—0
A Limited-Capacity Minimax Theorem for Non-Convex Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets—0
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