Examples

Walkthroughs and tutorials for using Eggroll Trainer.

Available Examples

• Basic Usage - Simple example with both trainers
• MNIST Classification - Full EGGROLL vs SGD comparison
• Custom Trainers - Creating your own ES algorithm

3D Reinforcement Learning

Train agents in 3D MuJoCo environments:

See examples/animals_3d/ for 3D RL examples including: - Simple locomotion (Ant, HalfCheetah, Humanoid) - Multi-agent scenarios (flocking, predator-prey) - Advanced physics (underwater, aerial, cloth simulation)

Running Examples

All examples are in the examples/ directory:

# Basic example
python examples/basic_example.py

# MNIST comparison
python examples/mnist_comparison.py

# Multi-architecture comparison
python examples/run_all_comparisons.py

Example Structure

Each example demonstrates:

1. Model Definition - PyTorch model architecture
2. Fitness Function - How to evaluate model performance
3. Trainer Setup - Configuring the trainer
4. Training Loop - Running the training
5. Results - Analyzing performance

Quick Example

import torch
import torch.nn as nn
from eggroll_trainer import EGGROLLTrainer

# Model
class SimpleModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc = nn.Linear(10, 1)

    def forward(self, x):
        return self.fc(x)

# Fitness function
def fitness_fn(model):
    x = torch.randn(32, 10)
    y_pred = model(x)
    return y_pred.abs().mean().item()

# Train
model = SimpleModel()
trainer = EGGROLLTrainer(
    model.parameters(),
    model=model,
    fitness_fn=fitness_fn,
    population_size=256,
    learning_rate=0.01,
    sigma=0.1,
)

trainer.train(num_generations=100)

Next Steps

• Start with Basic Usage
• See User Guide for detailed explanations
• Check API Reference for API details