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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 76–100 of 311 papers

TitleStatusHype
A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement LearningCode0
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement LearningCode0
Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?Code1
Boosting Value Decomposition via Unit-Wise Attentive State Representation for Cooperative Multi-Agent Reinforcement Learning—0
SMAClite: A Lightweight Environment for Multi-Agent Reinforcement LearningCode1
Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information PrinciplesCode1
SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning—0
GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement LearningCode0
MAC-PO: Multi-Agent Experience Replay via Collective Priority OptimizationCode0
AIIR-MIX: Multi-Agent Reinforcement Learning Meets Attention Individual Intrinsic Reward Mixing Network—0
ReMIX: Regret Minimization for Monotonic Value Function Factorization in Multiagent Reinforcement Learning—0
Equivariant MuZero—0
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
PushWorld: A benchmark for manipulation planning with tools and movable obstaclesCode1
TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning ProblemsCode1
Self-Motivated Multi-Agent ExplorationCode0
Strangeness-driven Exploration in Multi-Agent Reinforcement LearningCode0
SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement LearningCode2
Hierarchical Strategies for Cooperative Multi-Agent Reinforcement Learning—0
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games—0
CURO: Curriculum Learning for Relative Overgeneralization—0
ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-DependencyCode2
Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning—0
Decision-making with Speculative Opponent ModelsCode0
Value-based CTDE Methods in Symmetric Two-team Markov Game: from Cooperation to Team CompetitionCode0
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