상세정보
학술대회
Multi-Agent Deep Reinforcement Learning using Attentive Graph Neural Architectures for Real-Time Strategy Games
- 저자
- 윤원준, 이성원, 김중헌
- 발행일
- 202110
- 출처
- International Conference on Systems, Man, and Cybernetics (SMC) 2021, pp.1-8
- 초록
- In real-time strategy (RTS) game artificial intelligence research, various multi-agent deep reinforcement learning (MADRL) algorithms are widely and actively used nowadays. Most of the research is based on StarCraft II environment because it is the most well-known RTS games in world-wide. In our proposed MADRL-based algorithm, distributed MADRL is fundamentally used that is called QMIX. In addition to QMIX-based distributed computation, we consider state categorization which is a novel preprocessing method for representation of graph attention. Furthermore, self-attention mechanisms are used for identifying the relationship among agents in the form of graphs. Based on these approaches, we propose a categorized state graph attention policy (CSGA-policy). As observed in the performance evaluation of our proposed CSGA-policy with the most well-known StarCraft II simulation environment, our proposed algorithm works well in various settings, as expected.
- KSP 제안 키워드
- Attention mechanism, Deep reinforcement learning, Distributed Computation, Performance evaluation, RTS games, Real-time strategy games, Reinforcement Learning(RL), Simulation Environment, StarCraft II, State graph, World-wide