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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 51–75 of 175 papers

TitleStatusHype
TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning ProblemsCode1
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
On Efficient Reinforcement Learning for Full-length Game of StarCraft IICode2
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
Transformer-based Value Function Decomposition for Cooperative Multi-agent Reinforcement Learning in StarCraftCode1
Unsupervised Hebbian Learning on Point Sets in StarCraft II—0
SC2EGSet: StarCraft II Esport Replay and Game-state DatasetCode1
PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement LearningCode0
Evolutionary Game-Theoretical Analysis for General Multiplayer Asymmetric Games—0
On the Limitations of Elo: Real-World Games, are Transitive, not AdditiveCode0
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay BufferCode1
S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?—0
Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis—0
Off-Beat Multi-Agent Reinforcement Learning—0
Learning to Guide Multiple Heterogeneous Actors from a Single Human Demonstration via Automatic Curriculum Learning in StarCraft II—0
LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning—0
Learning to Transfer Role Assignment Across Team Sizes—0
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed AgentsCode0
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
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