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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 276–300 of 311 papers

TitleStatusHype
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
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