---
language: en
license: apache-2.0
library_name: jax
tags:
- deep-reinforcement-learning
- neuroevolution
- tpu
- autonomous-agents
- artificial-life
datasets:
- synthetic-navigation-sim
metrics:
- foraging-efficiency
- pathfinding-success
---

# 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.

```python
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)