Knowledge-Guided Agent-Tactic-Aware Learning for StarCraft Micromanagement

By Yue Hu, Juntao Li, Xi Li, G. Pan, Mingliang Xu. 2018

14 citations recorded by the source corpus.

As an important and challenging problem in artificial intelligence (AI) game playing, StarCraft micromanagement involves a dynamically adversarial game playing process with complex multi-agent control within a large action space. In this paper, we propose a novel knowledge-guided agent-tactic-aware learning scheme, that is, opponent-guided tactic learning (OGTL), to cope with this micromanagement problem. In principle, the proposed scheme takes a two-stage cascaded learning strategy which is capable of not only transferring the human tactic knowledge from the human-made opponent agents to our AI agents but also improving the adversarial ability. With the power of reinforcement learning, such a knowledge-guided agent-tactic-aware scheme has the ability to guide the AI agents to achieve high winning-rate performances while accelerating the policy exploration process in a tactic-interpretable fashion. Experimental results demonstrate the effectiveness of the proposed scheme against the state-of-the-art approaches in several benchmark combat scenarios.

Subfields: Artificial Intelligence, Sociology and Political Science

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Citations in the corpus

The StarCraft Multi-Agent Challenge (2019, background): “…tasks has thus been steadily appearing (Usunier et al., 2016; Foerster et al., 2017, 2018a; Rashid et al., 2018; Nardelli et al., 2018; Hu et al., 2018; Shao et al., 2018; Foerster et al., 2018b) with the release of TorchCraft (Synnaeve et al., 2016) and SC2LE (Vinyals et al., 2017),…”