Combining Reinforcement Learning and Behavior Trees for NPCs in Video Games with AMD Schola
This addresses practical challenges for the Game AI community in integrating RL into video games, though it appears incremental as it builds on existing suggestions for BT+RL intersections.
The paper tackled the slow adoption of reinforcement learning (RL) in commercial video games by exploring the combination of RL with behavior trees (BTs) for NPCs, demonstrating viability using AMD Schola in a complex 3D environment inspired by 'The Last of Us'.
While the rapid advancements in the reinforcement learning (RL) research community have been remarkable, the adoption in commercial video games remains slow. In this paper, we outline common challenges the Game AI community faces when using RL-driven NPCs in practice, and highlight the intersection of RL with traditional behavior trees (BTs) as a crucial juncture to be explored further. Although the BT+RL intersection has been suggested in several research papers, its adoption is rare. We demonstrate the viability of this approach using AMD Schola -- a plugin for training RL agents in Unreal Engine -- by creating multi-task NPCs in a complex 3D environment inspired by the commercial video game ``The Last of Us". We provide detailed methodologies for jointly training RL models with BTs while showcasing various skills.