Reviewing different Neural Network Models for Multi-Agent games on Arm using Unity
September 12, 2023
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
- NFC wireless interface supporting ISO14443 A and B with EEPROM on SMIC 180nm
- DDR5 MRDIMM PHY and Controller
- RVA23, Multi-cluster, Hypervisor and Android
- HBM4E PHY and controller
- LZ4/Snappy Data Compressor
Related Blogs
- Benefit of pruning and clustering a neural network for before deploying on Arm Ethos-U NPU
- Neural Network Model quantization on mobile
- Pace of Innovation for Custom Silicon on Arm Continues with AWS Graviton4
- Windows on Arm is Ready for Prime Time: Native Chrome Caps Momentum for the Future of Laptop Computing
Latest Blogs
- lowRISC Tackles Post-Quantum Cryptography Challenges through Research Collaborations
- How to Solve the Size, Weight, Power and Cooling Challenge in Radar & Radio Frequency Modulation Classification
- Programmable Hardware Delivers 10,000X Improvement in Verification Speed over Software for Forward Error Correction
- The Integrated Design Challenge: Developing Chip, Software, and System in Unison
- Introducing Mi-V RV32 v4.0 Soft Processor: Enhanced RISC-V Power