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Code to reproduce the experiments in Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation (MEEE).
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# Instruction to reproduce MEEE
Code to reproduce the experiments in [Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation](https://arxiv.org/pdf/2107.01825.pdf)[[abs]](https://arxiv.org/abs/2107.01825).
It is noteworthy that our code is mainly based on MBPO, and we refer interested readers to the original code base [MBPO](https://github.com/JannerM/mbpo) for more details.
## Installation
1. Install `MuJoCo 2.0` at `~/.mujoco/mujoco200` and copy your license key to `~/.mujoco/mjkey.txt`, for example, you need to install the following
dependencies first for Linux platform:
```
sudo yum install patchelf
sudo yum install mesa-libGL-devel mesa-libGLU-devel
sudo yum install mesa-libOSMesa-devel
sudo yum install mesa-libOSMesa
sudo yum install glfw
sudo yum install mesa-libGL
sudo yum install openmpi-devel
```
2. Create a conda environment and install dependencies in `requirements.txt`
```python
cd code_meee
conda create -n "your_env_name" python=3.6
conda activate "your_env_name"
# install cuda to suport tf-gpu==1.13.1
conda install cudatoolkit==10.0.130
conda install cudnn==7.6.5
pip install -r requirements.txt
```
## Usage
Configuration files can be found in `examples/config`. Use the following command to conduct experiment on Humanoid-v2:
```python
python main.py run_local examples.development --config=examples.config.humanoid.1 --trial-gpus=1
```
Currently only running locally is supported, so just keep the `run_local` and `examples.development` arguments. `examples.config.humanoid.1` determines the configuration file you want to use, and `--trial-gpus=1` indicate that you would like to experiment with one Nvidia GPU, you could change the experiment environment and GPU used by modifying relative arguments.
## Logging
The results can be found in the default directory `log_dir=~/ray_meee/`, you could also specify the directory in `examples/config/configuration_files`.
## Citation
If you use this code or results in your paper, please cite our work as:
```
@inproceedings{yao2021sample,
title={Sample efficient reinforcement learning via model-ensemble exploration and exploitation},
author={Yao, Yao and Xiao, Li and An, Zhicheng and Zhang, Wanpeng and Luo, Dijun},
booktitle={2021 IEEE International Conference on Robotics and Automation (ICRA)},
pages={4202--4208},
year={2021},
organization={IEEE}
}
```
## License
The code in this repository is released under the MIT license as found in the
[LICENSE](LICENSE) file.
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