Applying Supervised And Reinforcement Learning Methods To Create Neural-network-based Agents For Playing Starcraft II
2021 Β· MichaΕ Opanowicz
Abstract
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 be
Authors
(none)
Tags
Stats
Related papers
- SCC: An Efficient Deep Reinforcement Learning Agent Mastering The Game Of Starcraft II (2020)0.00
- Tstarbot-x: An Open-sourced And Comprehensive Study For Efficient League Training In Starcraft II Full Game (2020)0.00
- Starcraft Micromanagement With Reinforcement Learning And Curriculum Transfer Learning (2018)16.19
- Efficient Reinforcement Learning For Starcraft By Abstract Forward Models And Transfer Learning (2019)8.60
- Starcraft II: A New Challenge For Reinforcement Learning (2017)0.00
- Tstarbots: Defeating The Cheating Level Builtin AI In Starcraft II In The Full Game (2018)0.00
- Macro Action Selection With Deep Reinforcement Learning In Starcraft (2018)9.92
- Multi-agent Deep Reinforcement Learning Using Attentive Graph Neural Architectures For Real-time Strategy Games (2021)7.16