Implementation of World Models paper
Initially we collect rollouts of the environment using a random policy.
python datasets/carracing.py
Generated rollouts will be placed in random directory in data/carracing folder
Later we train all the three modules i.e., VAE module, Memory module and Controller module independently
python src/train_convVAE.py
python src/train_lstm.py
python src/evolution_pooling.py
To test average rewards in gym environment
python src/test_gym.py
To visualise vae original and reconstructed images for analysis
python utils/visualize_vae.py
To plot the graphs from the controller training for analysis
Run utils/plot_utility.ipynb file
Logging from model training are available in logs folder
Saved model checkpoints are available in checkpoints folder
We have referred ctallec repo