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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 76–100 of 175 papers

TitleStatusHype
COA-GPT: Generative Pre-trained Transformers for Accelerated Course of Action Development in Military Operations—0
BET: Explaining Deep Reinforcement Learning through The Error-Prone Decisions—0
StarCraftImage: A Dataset For Prototyping Spatial Reasoning Methods For Multi-Agent Environments—0
DCIR: Dynamic Consistency Intrinsic Reward for Multi-Agent Reinforcement Learning—0
CODEX: A Cluster-Based Method for Explainable Reinforcement LearningCode0
Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play—0
Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization—0
Fidelity-Induced Interpretable Policy Extraction for Reinforcement Learning—0
Leveraging World Model Disentanglement in Value-Based Multi-Agent Reinforcement Learning—0
Never Explore Repeatedly in Multi-Agent Reinforcement Learning—0
Offline Multi-Agent Reinforcement Learning with Coupled Value Factorization—0
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement LearningCode0
Boosting Value Decomposition via Unit-Wise Attentive State Representation for Cooperative Multi-Agent Reinforcement Learning—0
SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning—0
AIIR-MIX: Multi-Agent Reinforcement Learning Meets Attention Individual Intrinsic Reward Mixing Network—0
Self-Motivated Multi-Agent ExplorationCode0
CURO: Curriculum Learning for Relative Overgeneralization—0
Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning—0
Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy GradientCode0
MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees—0
Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction—0
Forecasting Evolution of Clusters in Game Agents with Hebbian Learning—0
A Framework for Understanding and Visualizing Strategies of RL AgentsCode0
Unsupervised Hebbian Learning on Point Sets in StarCraft II—0
Evolutionary Game-Theoretical Analysis for General Multiplayer Asymmetric Games—0
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