3.2 Optimization Strategies
This section outlines the optimization strategies implemented to enhance the performance, efficiency, and reliability of the Neural Snake AI system.
3.2.1 Neural Network Optimization
Weight Optimization
class NetworkOptimizer { // Implement batch normalization batchNormalize(layer) { const mean = layer.reduce((a, b) => a + b) / layer.length; const variance = layer.reduce((a, b) => a + Math.pow(b - mean, 2)) / layer.length; return layer.map(value => (value - mean) / Math.sqrt(variance + 1e-8)); } // Implement dropout for regularization applyDropout(layer, rate = 0.5) { const mask = layer.map(() => Math.random() > rate ? 1 / (1 - rate) : 0); return layer.map((value, i) => value * mask[i]); } // Optimize learning rate adaptiveLearningRate(iteration, baseRate = 0.1) { return baseRate / (1 + 0.01 * iteration); } }Memory Management
class MemoryOptimizer { constructor(maxCacheSize = 1000) { this.cache = new LRUCache(maxCacheSize); this.weightHistory = []; } // Implement weight pruning pruneWeights(weights, threshold = 0.01) { return weights.map(w => Math.abs(w) < threshold ? 0 : w); } // Optimize cache usage manageCache() { if (this.cache.size > this.cache.maxSize * 0.9) { this.cache.prune(); } } }
3.2.2 Game Engine Optimization
Rendering Optimization
Physics Optimization
3.2.3 Blockchain Integration Optimization
Transaction Processing
State Synchronization
3.2.4 Performance Monitoring
3.2.5 Resource Management
Asset Loading
Memory Management
These optimization strategies work together to ensure the system operates at peak efficiency while maintaining reliability and responsiveness. Regular monitoring and adjustment of these optimizations ensure continued performance improvements over time.