Code for MADE: Exploration via Maximizing Deviation from Explored Regions
In this repository, we provide code for MADE algorithm in the paper mentioned above. The corresponding code is in sub-directory dreamer_made and rad_made. We also provide several example scripts in the directories.
If you find this repository useful for your research, please cite:
@article{zhang2021made,
title={MADE: Exploration via Maximizing Deviation from Explored Regions},
author={Zhang, Tianjun and Rashidinejad, Paria and Jiao, Jiantao and Tian, Yuandong and Gonzalez, Joseph and Russell, Stuart},
journal={arXiv preprint arXiv:2106.10268},
year={2021}
}
You can also install custom version of dm_control to run experiments on Walker_Run_Sparse and Cheetah_Run_Sparse. You could do this by following command:
cd ../envs/dm_control
pip install .
conda env install -f conda_env.yml
python train.py env=hopper_hop batch_size=512 action_repeat=2 logdir=runs_rad_made beta_init=0.5
Our code is built on top of the Dreamer repository.
You could install all dependencies by following command:
pip install --user tensorflow-gpu==2.2.0
pip install --user tensorflow_probability
pip install --user git+git://github.com/deepmind/dm_control.git
pip install --user pandas
pip install --user matplotlib
# Install custom dm_control environments for walker_run_sparse and cheetah_run_sparse
cd ../envs/dm_control
pip install .
python dreamer.py --logdir ./logdir/dmc_pendulum_swingup/dreamer_made/0 --task dmc_pendulum_swingup --seed 0 --beta 0.1
Our code is built on top of the DrQ, Dreamer and RE3repository.
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