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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 151175 of 311 papers

TitleStatusHype
Containerized Distributed Value-Based Multi-Agent Reinforcement Learning0
HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism0
Leveraging Transformers for StarCraft Macromanagement Prediction0
TiKick: Towards Playing Multi-agent Football Full Games from Single-agent DemonstrationsCode1
No-Press Diplomacy from ScratchCode1
Divergence-Regularized Multi-Agent Actor-Critic0
Revisiting the Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning0
Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning0
Role Diversity Matters: A Study of Cooperative Training Strategies for Multi-Agent RL0
MARNET: Backdoor Attacks against Value-Decomposition Multi-Agent Reinforcement Learning0
Applying supervised and reinforcement learning methods to create neural-network-based agents for playing StarCraft IICode1
Influence-Based Reinforcement Learning for Intrinsically-Motivated Agents0
Adversary agent reinforcement learning for pursuit-evasion0
Settling the Variance of Multi-Agent Policy GradientsCode1
An Approach to Partial Observability in Games: Learning to Both Act and Observe0
Rethinking of AlphaStarCode1
Perceiver IO: A General Architecture for Structured Inputs & OutputsCode1
Cooperative Exploration for Multi-Agent Deep Reinforcement Learning0
Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi0
MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning0
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
Truthful Self-Play0
Context-Aware Sparse Deep Coordination GraphsCode1
Celebrating Diversity in Shared Multi-Agent Reinforcement LearningCode1
Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment0
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