Basem1166/world-models-Breakout

World Models - Atari Agent

This is a World Models implementation from Ha & Schmidhuber (2018) trained on Atari Breakout environment.

Model Description

The World Models architecture consists of three main components:

1. VAE (Variational Autoencoder): Compresses 64x64 RGB images into a 64-dimensional latent space
2. RNN (Memory-Augmented Recurrent Neural Network - MDRNN): Predicts the next latent representation given current latent state and action
3. Controller: A linear controller optimized using CMA-ES to maximize cumulative reward

Model Details

• Latent Size: 64
• Hidden Size: 256 (MDRNN)
• Action Space: 4 (Atari discrete actions)
• Architecture: Convolutional encoder/decoder for VAE, LSTM-based RNN
• Optimization: CMA-ES for controller training

Usage

import torch
from pathlib import Path

# Load checkpoint
checkpoint = torch.load('pytorch_model.bin')

# Access components
vae_state = checkpoint['vae_state_dict']
rnn_state = checkpoint['rnn_state_dict']
controller_state = checkpoint['controller_state_dict']

# Reconstruct models (see auto_train.py for architecture definitions)
# and load states into them

Training Details

• Environment: Atari Breakout
• Harvest Episodes: 100
• VAE Epochs: 20
• RNN Epochs: 20
• CMA-ES Generations: 25
• Population Size: 32
• Framework: PyTorch
• Training Script: auto_train.py

References

• Ha, D., & Schmidhuber, J. (2018). World Models. arXiv preprint arXiv:1803.10122.
• Original paper: https://worldmodels.github.io/

License

MIT License