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
- MIL-STD-1553 Controller IP
- UFS 5.x Device IP
- UCIe 3.x Controller IP
- Ethernet 800G PCS IP
- CHI to UCIe Bridge 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
- Cadence Extends Support for Automotive Solutions on Arm Zena Compute Subsystems
- Running LSTM neural networks on an Imagination NNA
Latest Blogs
- CDM Dependence on Device Capacitance
- What the Cyber Resilience Act means for the future of chip design
- When Your IP Vendor Has Operated 150,000 Base Stations: Introducing Viettel Semiconductor
- Relationship between architecture and validation in system design
- The Post-Quantum Cryptography Mandate: Building Cryptographically Agile Systems for the Quantum Era