Code structure is similar to original work by Danijar Hafner in Tensorflow
dreamer.py - main function for training and evaluating dreamer agent
utils.py - Logger, miscallaneous utility functions
models.py - All the networks for world model and actor are implemented here
replay_buffer.py - Experience buffer for training world model
env_wrapper.py - Gym wrapper for Dm_control suite
All the hyperparameters are listed in main.py and are avaialble as command line args.
python dreamer.py --env 'walker-walk' --algo 'Dreamerv1' --exp 'default_hp' --train
python dreamer.py --env 'walker-walk' --algo 'Dreamerv1' --exp 'eval' --evaluate --restore --checkpoint_path '<your_ckpt_path>'