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.

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