(Linux only) Create test_env environment by either:

cd code && sh setup.sh

or:

cd code
conda create --name test_env --file environment.yml
conda activate test_env
pip install -r requirements.txt

then install MuJoCo as instruction

Our ALH is implemented in file ./code/online/memTD3.py. The adaptive rollout and two variants of discovery scheme are described in both files ./code/online/main.py (Mujoco test) and ./code/online/main_toy.py (MultiNormEnv analysis). For a clear presentation, please refer to our paper.

conda activate test_env
cd code/online && sh run_experiments_toy.sh [n]

where [n] is the number of parallel processes. If the machine does not have gpu [n], the total number of parallel processes is equal to [n]. If the machine has multiple gpus, the total number of parallel processes is equal to [n] x [number of gpus]

conda activate test_env
cd code/online && sh run_experiments.sh [n]

Our ALH is implemented in file ./code/offline/memTD3.py. The adaptive rollout is described in file ./code/offline/evaluate.py.

conda activate test_env
cd code/offline && sh run_experiments.sh [n]

To be fairly compared with TD3/TD3+BC, our implementation bases on author implementation of:

@inproceedings{fujimoto2018addressing,
    title={Addressing Function Approximation Error in Actor-Critic Methods},
    author={Fujimoto, Scott and Hoof, Herke and Meger, David},
    booktitle={International Conference on Machine Learning},
    pages={1582--1591},
    year={2018}
}
@inproceedings{fujimoto2021minimalist,
    title={A Minimalist Approach to Offline Reinforcement Learning},
    author={Scott Fujimoto and Shixiang Shane Gu},
    booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
    year={2021},
}

And compares with implementation in:

@inproceedings{janner2019mbpo,
    author = {Michael Janner and Justin Fu and Marvin Zhang and Sergey Levine},
    title = {When to Trust Your Model: Model-Based Policy Optimization},
    booktitle = {Advances in Neural Information Processing Systems},
    year = {2019}
}
@misc{pytorch_minimal_ppo,
    author = {Barhate, Nikhil},
    title = {Minimal PyTorch Implementation of Proximal Policy Optimization},
    year = {2021},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/nikhilbarhate99/PPO-PyTorch}},
}

Reproducing all experiments can take about several thousands of GPU hour. If you only want to compare with our results, we publish all result files in ./results. For visualization (tables, figures) reported in our paper, refer to ./plot.

We provide code to reproduce our all reported figures in our paper. Please refer to this notebook file

We provide descriptions for our extra experiments in our appendix. Please refer to this notebook file