Examples

Home – Building Ball Shooter (Schola)

This guide walks you through creating a simple shooting range environment and training an agent to shoot moving targets in Unreal Engine. The goal is to aim correctly before shooting and take down targets one by one.

Home – Building Tag (Schola)

In this tutorial, we create a multi-agent environment where the agents are trained to play a 3v1 game of tag. Specifically, we create one runner agent which tries to avoid being caught and three tagger agents with the goal of catching the runner. The…

Home – Building Maze Solver (Schola)

This guide walks you through creating a maze environment and training an agent to navigate it using reinforcement learning in Unreal Engine.

AMD Schola

AMD Schola is a library for developing reinforcement learning (RL) agents in Unreal Engine and training with your favorite python-based RL Frameworks.

Advancing AI in Video Games with AMD Schola

By connecting popular open-source RL libraries (written in Python) with the visual and physics capabilities of Unreal Engine, Schola empowers AI researchers and game developers alike to push the boundaries of intelligent gameplay.

AMD matrix cores (amd-lab-notes)

This first post in the ‘AMD lab notes’ series takes a look at AMD’s Matrix Core technology and how best to use it to speed up your matrix operations.

Affinity part 2 – System topology and controlling affinity – AMD lab notes

This second part introduces common tools to understand the topology of your system and to control affinity for different applications

AMD GCN Assembly: Cross-Lane Operations

Cross-lane operations are an efficient way to share data between wavefront lanes. This article covers in detail the cross-lane features that GCN3 offers.