# Combining Reinforcement Learning and Behavior Trees for NPCs in Video Games with AMD Schola

arXiv: https://arxiv.org/abs/2510.14154 · HTML: https://mlsanity.com/paper/2510.14154/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 15 Oct 2025.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 13 of 13)

- [A Generalist Agent](https://mlsanity.com/paper/2205.06175/) (2022)
- [Emergent Tool Use From Multi-Agent Autocurricula](https://mlsanity.com/paper/1909.07528/) (2019)
- [Multi-Task Reinforcement Learning with Context-based Representations](https://mlsanity.com/paper/2102.06177/) (2021)
- [Counter-Strike Deathmatch with Large-Scale Behavioural Cloning](https://mlsanity.com/paper/2104.04258/) (2021)
- [Sharing Knowledge in Multi-Task Deep Reinforcement Learning](https://mlsanity.com/paper/2401.09561/) (2024)
- [Deep Reinforcement Learning for Navigation in AAA Video Games](https://mlsanity.com/paper/2011.04764/) (2020)
- ["It's Unwieldy and It Takes a Lot of Time." Challenges and Opportunities for Creating Agents in Commercial Games](https://mlsanity.com/paper/2009.00541/) (2020)
- [Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games](https://mlsanity.com/paper/2303.02160/) (2023)
- [A Framework for Constrained and Adaptive Behavior-Based Agents](https://mlsanity.com/paper/1506.02312/) (2015)
- [Multi-Task Reinforcement Learning Enables Parameter Scaling](https://mlsanity.com/paper/2503.05126/) (2025)
- [Adding Neural Network Controllers to Behavior Trees without Destroying Performance Guarantees](https://mlsanity.com/paper/1809.10283/) (2018)
- [Training Interactive Agent in Large FPS Game Map with Rule-enhanced Reinforcement Learning](https://mlsanity.com/paper/2410.04936/) (2024)
- [Path Generation and Evaluation in Video Games: A Nonparametric Statistical Approach](https://mlsanity.com/paper/2506.03522/) (2025)

## Abstract (arXiv)

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.