Dreamer

Dreamer is a visual Model-Based Reinforcement algorithm, that learns a world model which captures latent dynamics from high-level pixel images and trains a control agent entirely in imagined rollouts from the learned world model.

This work is my attempt at reproducing Dreamerv1 & v2 papers in pytorch specifically for continuous control tasks in deepmind control suite.

Differences in implementation:

1. Replay Buffer manages episodes instead of transitions, making sure that we don't mix episodes when sampling
2. Although less flexible, Convolution models where layed out step by step for readibility

Code Structure

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, miscellaneous utility functions

models.py - All the NNs for world model and actor

replay_buffer.py - Experience buffer for training world model

env_wrapper.py - Gym wrapper for Dm_control suite

Runs can be configured from the config.json

Installation

Run: conda env create -f environment.yml

For training

python dreamer.py --config config.json --env <env-name> --train

For Evaluation

python dreamer.py --config config.json --evaluate --env <env-name> --checkpoint_path '<your_ckpt_path>'

Acknowledgements

This code is heavily inspired by following open-source works

dreamer by Danijar Hafner : https://github.com/danijar/dreamer

dreamer-pytorch by yusukeurakami : https://github.com/yusukeurakami/dreamer-pytorch

Dreamerv2 by Rajghugare : https://github.com/RajGhugare19/dreamerv2

Dreamer by adityabingi : https://github.com/adityabingi/Dreamer