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
- Multi-channel, multi-rate Ethernet aggregator - 10G to 400G AX (e.g., AI)
- Multi-channel, multi-rate Ethernet aggregator - 10G to 800G DX
- 200G/400G/800G Ethernet PCS/FEC
- 50G/100G MAC/PCS/FEC
- 25G/10G/SGMII/ 1000BASE-X PCS and MAC
Related Blogs
- Benefit of pruning and clustering a neural network for before deploying on Arm Ethos-U NPU
- Neural Network Model quantization on mobile
- Develop Software for the Cortex-M Security Extensions Using Arm DS and Arm GNU Toolchain
- Efficiently Packing Neural Network AI Model for the Edge