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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 201–250 of 311 papers

TitleStatusHype
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
Grounding Natural Language Commands to StarCraft II Game States for Narration-Guided Reinforcement Learning—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
S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?—0
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II—0
Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction—0
Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients—0
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning—0
SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning—0
SMAUG: A Sliding Multidimensional Task Window-Based MARL Framework for Adaptive Real-Time Subtask Recognition—0
Regularized Softmax Deep Multi-Agent Q-Learning—0
StarCraft II Build Order Optimization using Deep Reinforcement Learning and Monte-Carlo Tree Search—0
StarCraftImage: A Dataset For Prototyping Spatial Reasoning Methods For Multi-Agent Environments—0
State-based Episodic Memory for Multi-Agent Reinforcement Learning—0
Superhuman Game AI Disclosure: Expertise and Context Moderate Effects on Trust and Fairness—0
SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning—0
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games—0
The Adversarial Resilience Learning Architecture for AI-based Modelling, Exploration, and Operation of Complex Cyber-Physical Systems—0
Towards a Deep Reinforcement Learning Approach for Tower Line Wars—0
Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization—0
Towards Understanding Linear Value Decomposition in Cooperative Multi-Agent Q-Learning—0
Transferable Curricula through Difficulty Conditioned Generators—0
Tree Search for Simultaneous Move Games via Equilibrium Approximation—0
Truthful Self-Play—0
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning—0
Unsupervised Hebbian Learning on Point Sets in StarCraft II—0
Value Propagation Networks—0
Variational Offline Multi-agent Skill Discovery—0
"Weak AI" is Likely to Never Become "Strong AI", So What is its Greatest Value for us?—0
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement LearningCode0
Clear the Fog: Combat Value Assessment in Incomplete Information Games with Convolutional Encoder-DecodersCode0
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