Learning to Guide Multiple Heterogeneous Actors from a Single Human Demonstration via Automatic Curriculum Learning in StarCraft II

no code implementations • 11 May 2022 • Nicholas Waytowich, James Hare, Vinicius G. Goecks, Mark Mittrick, John Richardson, Anjon Basak, Derrik E. Asher

Traditionally, learning from human demonstrations via direct behavior cloning can lead to high-performance policies given that the algorithm has access to large amounts of high-quality data covering the most likely scenarios to be encountered when the agent is operating.

reinforcement-learning Reinforcement Learning (RL) +2

On games and simulators as a platform for development of artificial intelligence for command and control

no code implementations • 21 Oct 2021 • Vinicius G. Goecks, Nicholas Waytowich, Derrik E. Asher, Song Jun Park, Mark Mittrick, John Richardson, Manuel Vindiola, Anne Logie, Mark Dennison, Theron Trout, Priya Narayanan, Alexander Kott

Games and simulators can be a valuable platform to execute complex multi-agent, multiplayer, imperfect information scenarios with significant parallels to military applications: multiple participants manage resources and make decisions that command assets to secure specific areas of a map or neutralize opposing forces.

Starcraft Starcraft II