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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 126–150 of 311 papers

TitleStatusHype
Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning—0
Composable Planning with Attributes—0
Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi—0
Equivariant MuZero—0
Combat Models for RTS Games—0
AI and Wargaming—0
Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play—0
MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees—0
Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks—0
ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization—0
Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation—0
Collaborative AI Teaming in Unknown Environments via Active Goal Deduction—0
Approximation Models of Combat in StarCraft 2—0
COA-GPT: Generative Pre-trained Transformers for Accelerated Course of Action Development in Military Operations—0
MARNET: Backdoor Attacks against Value-Decomposition Multi-Agent Reinforcement Learning—0
Efficiently Scanning and Resampling Spatio-Temporal Tasks with Irregular Observations—0
Maximum Correntropy Value Decomposition for Multi-agent Deep Reinforcemen Learning—0
Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning—0
Dynamic Sight Range Selection in Multi-Agent Reinforcement Learning—0
MAIDCRL: Semi-centralized Multi-Agent Influence Dense-CNN Reinforcement Learning—0
Dungeon Crawl Stone Soup as an Evaluation Domain for Artificial Intelligence—0
DOP: Off-Policy Multi-Agent Decomposed Policy Gradients—0
Anticipatory Thinking Challenges in Open Worlds: Risk Management—0
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning—0
MARL-LNS: Cooperative Multi-agent Reinforcement Learning via Large Neighborhoods Search—0
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