DeepRL (Standard Edition)

DeepRL is a robust, neuroevolutionary navigation agent. Unlike the "Micro" tier, the Standard Edition is designed for higher-resolution environments (64x64) and possesses enough neural capacity for active pathfinding and "hunting" behaviors.

📊 Model Profile

Feature	Specification
Model Architecture	3-Layer Deep CNN (64-Filter Width)
Parameters	45,000 (180 KiB)
Grid Resolution	64x64 Cellular Automata
Input Channels	6 (Life, Food, Lava, 3x Signaling)
Training Steps	48,000+ Generations
Compute	16x Google Cloud TPU v5e (TRC Program)

🧬 Key Behavioral Upgrades

The Standard Edition introduces Metabolism Decay. Unlike previous iterations that could "camp" on a single food source, this model must actively navigate to new food patches to maintain its mass.

• Advanced Pathfinding: Capable of detecting food sources up to 32 pixels away.
• Lava kiting: Uses a wider 64-filter "vision" to weave through complex obstacle patterns.
• Mass Regulation: Emergent behavior allows the agent to stabilize its size relative to available resources.

🚀 Deployment (Inference)

This model is optimized for modern consumer CPUs. It is designed to run smoothly at 30-60 FPS on mid-tier hardware.

Hardware Target: "Desktop Tier"

• CPU: Intel Core i5/i7 (8th Gen+) or AMD Ryzen 5/7
• RAM: 8GB - 16GB
• Platform: Works with any NumPy-compatible environment (Python 3.8+).

🛠️ Loading the DNA

The weights are stored as a ragged NumPy object array to preserve the specific convolutional shapes required by the JAX/Keras training pipeline.

import numpy as np

# Load the Standard DNA payload (Apache 2.0 Licensed)
dna = np.load("DeepRL_Final_Gen48k.npy", allow_pickle=True)

# Weight Indices:
# [0, 1] - Conv Block 1 (64 Filters, 3x3)
# [2, 3] - Conv Block 2 (64 Filters, 1x1)
# [4, 5] - Decision Output (6 Channels, 1x1)