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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Methods used in the Paper


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