A decision-making framework using MCTS as a hierarchical task network and deep learning connector

Currently, purely deep learning-based agents struggle to make optimal decisions within a short timeframe in problems with a vast decision-making space. Human planning knowledge is required to assist agents in making better decisions. This manuscript proposes a novel knowledge-guided and data-driven decision-making framework, utilizing hierarchical task network as the carrier of knowledge, deep learning as the trainer for data, and the Monte Carlo Tree Search as the connector between hierarchical task network and deep learning. The experiments on the MiniRTS environment validated that the proposed framework in this manuscript can replace humans in collecting high-quality data, and it can train neural networks that perform equally well as the compared network even with only 20% of the available data, which provide a new direction for future research.