metadata
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 | |
| 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