Applying supervised and reinforcement learning methods to create neural-network-based agents for playing StarCraft II
This work provides a baseline for small-scale experiments in real-time strategy games, making agent development more accessible to researchers with limited resources.
The authors tackled the problem of creating neural-network-based agents for playing StarCraft II without extensive expert knowledge or high computational resources, achieving non-trivial performance compared to in-game scripted bots while being trainable on a single consumer-grade PC with a single GPU.
Recently, multiple approaches for creating agents for playing various complex real-time computer games such as StarCraft II or Dota 2 were proposed, however, they either embed a significant amount of expert knowledge into the agent or use a prohibitively large for most researchers amount of computational resources. We propose a neural network architecture for playing the full two-player match of StarCraft II trained with general-purpose supervised and reinforcement learning, that can be trained on a single consumer-grade PC with a single GPU. We also show that our implementation achieves a non-trivial performance when compared to the in-game scripted bots. We make no simplifying assumptions about the game except for playing on a single chosen map, and we use very little expert knowledge. In principle, our approach can be applied to any RTS game with small modifications. While our results are far behind the state-of-the-art large-scale approaches in terms of the final performance, we believe our work can serve as a solid baseline for other small-scale experiments.