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DreamerPro

This is the official implementation of DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations in TensorFlow 2. A re-implementation of Temporal Predictive Coding for Model-Based Planning in Latent Space is also included.

DreamerPro makes large performance gains on the DeepMind Control suite both in the standard setting and when there are complex background distractions. This is achieved by combining Dreamer with prototypical representations that free the world model from reconstructing visual details.

<p align="center"> <img src="./plots/model.png" width="60%"> </p>

Setup

Dependencies

First clone the repository, and then set up a conda environment with all required dependencies using the requirements.txt file:

git clone https://github.com/fdeng18/dreamer-pro.git
cd dreamer-pro
conda create --name dreamer-pro python=3.8 conda-forge::cudatoolkit conda-forge::cudnn
conda activate dreamer-pro
pip install --upgrade pip
pip install -r requirements.txt

DreamerPro has not been tested on Atari, but if you would like to try, the Atari ROMs can be imported by following these instructions.

Natural background videos

Our natural background setting follows TPC. For convenience, we have included their code to download the background videos. Simply run:

python download_videos.py

This will download the background videos into kinetics400/videos.

Training

DreamerPro

For standard DMC, run:

cd DreamerPro
python dreamerv2/train.py --logdir log/dmc_{task}/dreamer_pro/{run} --task dmc_{task} --configs defaults dmc norm_off

Here, {task} should be replaced by the actual task, and {run} should be assigned an integer indicating the independent runs of the same model on the same task. For example, to start the first run on walker_run:

cd DreamerPro
python dreamerv2/train.py --logdir log/dmc_walker_run/dreamer_pro/1 --task dmc_walker_run --configs defaults dmc norm_off

For natural background DMC, run:

cd DreamerPro
python dreamerv2/train.py --logdir log/nat_{task}/dreamer_pro/{run} --task nat_{task} --configs defaults dmc reward_1000

TPC

DreamerPro is based on a newer version of Dreamer. For fair comparison, we re-implement TPC based on the same version. Our re-implementation obtains better results in the natural background setting than reported in the original TPC paper.

For standard DMC, run:

cd TPC
python dreamerv2/train.py --logdir log/dmc_{task}/tpc/{run} --task dmc_{task} --configs defaults dmc

For natural background DMC, run:

cd TPC
python dreamerv2/train.py --logdir log/nat_{task}/tpc/{run} --task nat_{task} --configs defaults dmc reward_1000

Dreamer

For standard DMC, run:

cd Dreamer
python dreamerv2/train.py --logdir log/dmc_{task}/dreamer/{run} --task dmc_{task} --configs defaults dmc

For natural background DMC, run:

cd Dreamer
python dreamerv2/train.py --logdir log/nat_{task}/dreamer/{run} --task nat_{task} --configs defaults dmc reward_1000 --precision 32

We find it necessary to use --precision 32 in the natural background setting for numerical stability.

Outputs

The training process can be monitored via TensorBoard. We have also included performance curves in plots. Note that these curves may appear different from what is shown in TensorBoard. This is because the evaluation return in the performance curves is averaged over 10 episodes, while TensorBoard only shows the evaluation return of the last episode.

Results

Standard DMC

<p align="center"> <img src="./plots/standard_dmc/curves.png" width="60%"> </p>

Natural Background DMC

<p align="center"> <img src="./plots/natural_background_dmc/curves.png" width="60%"> </p>

Acknowledgments

This repository is largely based on the TensorFlow 2 implementation of Dreamer. We would like to thank Danijar Hafner for releasing and updating his clean implementation. In addition, we also greatly appreciate the help from Tung Nguyen in implementing TPC.

Citation

@inproceedings{deng2022dreamerpro,
  title={Dreamerpro: Reconstruction-free model-based reinforcement learning with prototypical representations},
  author={Deng, Fei and Jang, Ingook and Ahn, Sungjin},
  booktitle={International Conference on Machine Learning},
  pages={4956--4975},
  year={2022},
  organization={PMLR}
}