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loayahmed123/
world-models-space3

Name: world-models-space3
Availability: InStock

pytorch

reinforcement-learning

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World Models for Space Invaders

This is a World Models agent trained on the SpaceInvadersNoFrameskip-v4 environment.

Model Description

World Models is a model-based reinforcement learning approach that learns a compressed representation of the environment and trains a controller to maximize reward in the learned model.

The architecture consists of three components:

V (Vision): Variational Autoencoder that compresses 64x64 RGB frames to 32-dimensional latent vectors
M (Memory): MDN-RNN that predicts the next latent state given current state and action
C (Controller): Linear policy trained with CMA-ES evolution strategy

Training Details

Hyperparameters

VAE Latent Dimension: 32
RNN Hidden Dimension: 256
Number of Gaussian Mixtures: 5
Population Size (CMA-ES): 64
Training Episodes: 100
VAE Epochs: 10
RNN Epochs: 20
Controller Generations: 10

Evaluation Results

Mean Reward: 155.00 ± 0.00
Max Reward: 155.00
Mean Episode Length: 1000.00

Usage

python

import torch
import gymnasium as gym

# Load models
vae = VAE(latent_dim=32)
vae.load_state_dict(torch.load('vae_model.pt'))

rnn = MDNRNN(latent_dim=32, action_dim=6)
rnn.load_state_dict(torch.load('mdnrnn_model.pt'))

controller = Controller(latent_dim=32, hidden_dim=256)
controller.load_state_dict(torch.load('controller_model.pt'))

# Run agent
env = gym.make('SpaceInvadersNoFrameskip-v4')
# ... (see repository for full inference code)

References

Paper: World Models (Ha & Schmidhuber, 2018)
Code: Based on the original World Models implementation

Citation

bibtex

@article{ha2018worldmodels,
  title={World Models},
  author={Ha, David and Schmidhuber, J{\"u}rgen},
  journal={arXiv preprint arXiv:1803.10122},
  year={2018}
}

loayahmed123/world-models-space3