Macro action selection with deep reinforcement learning in StarCraft

2 Dec 2018 • Sijia Xu • Hongyu Kuang • Zhi Zhuang • Renjie Hu • Yang Liu • Huyang Sun

StarCraft (SC) is one of the most popular and successful Real Time Strategy (RTS) games. In recent years, SC is also widely accepted as a challenging testbed for AI research because of its enormous state space, partially observed information, multi-agent collaboration, and so on... (read more)

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METHOD	TYPE
Double Q-learning	Off-Policy TD Control
Prioritized Experience Replay	Replay Memory
Dueling Network	Q-Learning Networks
N-step Returns	Value Function Estimation
Ape-X	Distributed Reinforcement Learning
Ape-X DQN	Q-Learning Networks
Q-Learning	Off-Policy TD Control
Dense Connections	Feedforward Networks
Convolution	Convolutions
DQN	Q-Learning Networks

METHOD

TYPE

Double Q-learning