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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 251–300 of 311 papers

TitleStatusHype
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning—0
Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning—0
MAIDCRL: Semi-centralized Multi-Agent Influence Dense-CNN Reinforcement Learning—0
MARL-LNS: Cooperative Multi-agent Reinforcement Learning via Large Neighborhoods Search—0
MARNET: Backdoor Attacks against Value-Decomposition Multi-Agent Reinforcement Learning—0
Maximum Correntropy Value Decomposition for Multi-agent Deep Reinforcemen Learning—0
Meta-Learning for Planning: Automatic Synthesis of Sample Based Planners—0
Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play—0
A Limited-Capacity Minimax Theorem for Non-Convex Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets—0
Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization—0
MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees—0
MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning—0
Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2—0
Modular Architecture for StarCraft II with Deep Reinforcement Learning—0
CGIBNet: Bandwidth-constrained Communication with Graph Information Bottleneck in Multi-Agent Reinforcement Learning—0
Multi-Agent Deep Reinforcement Learning using Attentive Graph Neural Architectures for Real-Time Strategy Games—0
Multi-agent Policy Optimization with Approximatively Synchronous Advantage Estimation—0
BGC: Multi-Agent Group Belief with Graph Clustering—0
Multi-objective evolution for 3D RTS Micro—0
NeuPL: Neural Population Learning—0
Grounding Natural Language Commands to StarCraft II Game States for Narration-Guided Reinforcement Learning—0
NeuroPAL: Punctuated Anytime Learning with Neuroevolution for Macromanagement in Starcraft: Brood War—0
Never Explore Repeatedly in Multi-Agent Reinforcement Learning—0
NQMIX: Non-monotonic Value Function Factorization for Deep Multi-Agent Reinforcement Learning—0
Off-Beat Multi-Agent Reinforcement Learning—0
Offline Multi-Agent Reinforcement Learning with Coupled Value Factorization—0
Offline-to-Online Multi-Agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration—0
On games and simulators as a platform for development of artificial intelligence for command and control—0
On Reinforcement Learning for Full-length Game of StarCraft—0
On Stateful Value Factorization in Multi-Agent Reinforcement Learning—0
Optimal Any-Angle Pathfinding on a Sphere—0
Optimize Neural Fictitious Self-Play in Regret Minimization Thinking—0
Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL—0
POWQMIX: Weighted Value Factorization with Potentially Optimal Joint Actions Recognition for Cooperative Multi-Agent Reinforcement Learning—0
Privacy-Engineered Value Decomposition Networks for Cooperative Multi-Agent Reinforcement Learning—0
QFree: A Universal Value Function Factorization for Multi-Agent Reinforcement Learning—0
QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning—0
QR-MIX: Distributional Value Function Factorisation for Cooperative Multi-Agent Reinforcement Learning—0
Reflection of Episodes: Learning to Play Game from Expert and Self Experiences—0
Reinforcement Learning-based Application Autoscaling in the Cloud: A Survey—0
Reinforcement Learning for the Beginning of Starcraft II Game—0
Reinforcement Learning of Implicit and Explicit Control Flow in Instructions—0
ReMIX: Regret Minimization for Monotonic Value Function Factorization in Multiagent Reinforcement Learning—0
Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning—0
Revisiting the Master-Slave Architecture in Multi-Agent Deep Reinforcement Learning—0
Revisiting the Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning—0
RMIX: Learning Risk-Sensitive Policies forCooperative Reinforcement Learning Agents—0
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents—0
RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning—0
Role Diversity Matters: A Study of Cooperative Training Strategies for Multi-Agent RL—0
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