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
- NPU IP
- JPEG XL Encoder
- I2C Master/Slave Controller Core
- NVMe Validation Test Suite
- Hybrid Memory Cube Verification 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
- Running LSTM neural networks on an Imagination NNA
- Take your neural networks to the next level with Arm's Machine Learning Inference Advisor
Latest Blogs
- Tape-Out Readiness Checklist: Engineering Decisions That Prevent Costly Respins
- How Cadence DSPs Put In-Cabin AI Audio On-Chip in SemiDrive's X10
- The Fastest Path to Scalable Photonic Systems: Using Proven IP for both PIC and EIC designs
- Building Trusted AI Agents from the Silicon Root of Trust
- Heterogeneous Computing in Space