Reviewing different Neural Network Models for Multi-Agent games on Arm using Unity
During the Game Developer Conference (GDC) in March 2023, we showcased our multi-agent demo called Candy Clash, a mobile game containing 100 intelligent agents. In the demo, the agents are developed using Unity’s ML-Agents Toolkit which allows us to train them using reinforcement learning (RL). To find out more about the demo and its development, see our previous blog series. Previously, the agents had a simple Multi-Layer Perceptron (MLP) Neural Network (NN) model. This blog explores the impact of using other types of neural networks models on the gaming experience and performance.
To read the full article, click here
Related Semiconductor IP
- LPDDR6/5X/5 PHY V2 - Intel 18A-P
- MIPI SoundWire I3S Peripheral IP
- LPDDR6/5X/5 Controller IP
- Post-Quantum ML-KEM IP Core
- MIPI SoundWire I3S Manager IP
Related Blogs
- Benefit of pruning and clustering a neural network for before deploying on Arm Ethos-U NPU
- Neural Network Model quantization on mobile
- New Armv9 CPUs for Accelerating AI on Mobile and Beyond
- Easing software development for high-performance zonal controller based on Arm Cortex-R82AE
Latest Blogs
- ML-DSA explained: Quantum-Safe digital Signatures for secure embedded Systems
- Efficiency Defines The Future Of Data Movement
- Why Standard-Cell Architecture Matters for Adaptable ASIC Designs
- ML-KEM explained: Quantum-safe Key Exchange for secure embedded Hardware
- Rivos Collaborates to Complete Secure Provisioning of Integrated OpenTitan Root of Trust During SoC Production
