---
language: en
license: apache-2.0
library_name: jax
tags:
- deep-reinforcement-learning
- resnet
- multi-agent-systems
- tpu
- swarm-intelligence
datasets:
- competitive-foraging-sim
metrics:
- multi-agent-survival
- resnet-efficiency
---

# Large-DeepRL (ResNet Edition)

**Large-DeepRL** is a high-capacity, multi-agent reinforcement learning model. It represents the "Predator" tier of the DeepRL evolution series, utilizing a **Residual Network (ResNet)** architecture to navigate a 128x128 high-resolution arena.

## 📊 Model Profile
| Feature | Specification |
| :--- | :--- |
| **Architecture** | Deep ResNet (Residual Skip Connections) |
| **Grid Resolution** | 128x128 (16,384 spatial cells) |
| **Parameters** | ~185,000 (~740 KiB) |
| **Agents** | 10 Competing Seeds per Environment |
| **Input Channels** | 8 (Life, Food, Lava, 5x Signaling/Memory) |
| **Training Steps** | Overnight Evolution (Gen 50k+) |
| **Compute** | 16x Google Cloud TPU v5e (TRC Program) |

## 🧬 Architectural Breakthroughs
This model moves beyond simple convolutions by implementing **Skip Connections**, allowing the gradient to flow through deeper layers without vanishing.

- **Global Spatial Reasoning:** The 128x128 grid provides 4x the territory of the Standard model, requiring the agent to plan long-distance paths.
- **Multi-Agent Competition:** Trained in a "scarcity" environment where 10 agents compete for limited food patches. This forces the emergence of aggressive, high-speed foraging behaviors.
- **8-Channel Alignment:** Optimized for TPU HBM alignment, ensuring maximum hardware utilization and zero memory padding bloat.

## 🚀 Deployment (Inference)
While technically runnable on high-end CPUs, this model is specifically targeted for **Low-End GPUs** to maintain real-time performance.

### Hardware Target: "GPU Tier"
- **Minimum GPU:** NVIDIA T4, RTX 3050, or equivalent.
- **Alternative:** High-end multi-core CPUs (AMD Ryzen 9 / Intel i9).
- **RAM:** 16GB minimum recommended.

## 🛠️ Loading the DNA
The model is saved as a structured NumPy object array. Note the 8-channel input requirement when setting up your inference environment.

```python
import numpy as np

# Load the Large-DeepRL DNA (Apache 2.0)
dna = np.load("Large-DeepRL.npy", allow_pickle=True)

# Architecture Structure:
# - Entry Convolution (64 filters)
# - ResNet Block 1 (Add + Activation)
# - ResNet Block 2 (Add + Activation)
# - 1x1 Strategy Head
# - 1x1 Decision Output