https://github.com/astorfi/dnc
Code for "Divide-and-Conquer Reinforcement Learning"
Science Score: 10.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
○codemeta.json file
-
○.zenodo.json file
-
○DOI references
-
✓Academic publication links
Links to: arxiv.org -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (11.9%) to scientific vocabulary
Last synced: 10 months ago
·
JSON representation
Repository
Code for "Divide-and-Conquer Reinforcement Learning"
Basic Info
- Host: GitHub
- Owner: astorfi
- Language: Python
- Default Branch: master
- Size: 29.5 MB
Statistics
- Stars: 0
- Watchers: 4
- Forks: 0
- Open Issues: 0
- Releases: 0
Fork of dibyaghosh/dnc
Created almost 8 years ago
· Last pushed about 8 years ago
https://github.com/astorfi/dnc/blob/master/
# Divide-and-Conquer Reinforcement Learning This repository contains code accompaning the paper, [Divide-and-Conquer Reinforcement Learning (Ghosh et al., ICLR 2018)](https://arxiv.org/abs/1711.09874). It includes code for the DnC algorithm, and the Mujoco environments used for the empirical evaluation. Please see [the project website](http://dibyaghosh.com/dnc/) for videos and further details.![]()
### Dependencies This codebase requires a valid installation of `rllab`. Please refer to the [rllab repository](https://github.com/rll/rllab) for installation instructions. The environments are built in Mujoco 1.31: follow the instructions [here](https://github.com/openai/mujoco-py/tree/0.5) to install Mujoco 1.31 if not already done. You are required to have a Mujoco license to run any of the environments. ### Usage Sample scripts for working with DnC and the provided environments can be found in the [examples](examples/) directory. In particular, a sample scripts for running DnC is located [here](examples/dnc_pick.py). ```bash source activate rllab_env python examples/dnc_pick.py ``` Environments are located in the [dnc/envs/](dnc/envs/) directory, and the DnC implementation can be found at [dnc/algos/](dnc/algos). ### Contact To ask questions or report issues, please open an issue on the [issues tracker](https://github.com/dibyaghosh/dnc/issues). ### Citing If you use DnC, please cite the following paper: - Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, Sergey Levine. "[Divide-and-Conquer Reinforcement Learning](https://arxiv.org/abs/1711.09874)". _Proceedings of the International Conference on Learning Representaions (ICLR), 2018._
Owner
- Name: Sina Torfi
- Login: astorfi
- Kind: user
- Location: San Jose
- Company: Meta
- Website: https://astorfi.github.io/
- Repositories: 196
- Profile: https://github.com/astorfi
PhD & Developer working on Deep Learning, Computer Vision & NLP
### Dependencies
This codebase requires a valid installation of `rllab`. Please refer to the [rllab repository](https://github.com/rll/rllab) for installation instructions.
The environments are built in Mujoco 1.31: follow the instructions [here](https://github.com/openai/mujoco-py/tree/0.5) to install Mujoco 1.31 if not already done. You are required to have a Mujoco license to run any of the environments.
### Usage
Sample scripts for working with DnC and the provided environments can be found in the [examples](examples/) directory. In particular, a sample scripts for running DnC is located [here](examples/dnc_pick.py).
```bash
source activate rllab_env
python examples/dnc_pick.py
```
Environments are located in the [dnc/envs/](dnc/envs/) directory, and the DnC implementation can be found at [dnc/algos/](dnc/algos).
### Contact
To ask questions or report issues, please open an issue on the [issues tracker](https://github.com/dibyaghosh/dnc/issues).
### Citing
If you use DnC, please cite the following paper:
- Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, Sergey Levine. "[Divide-and-Conquer Reinforcement Learning](https://arxiv.org/abs/1711.09874)". _Proceedings of the International Conference on Learning Representaions (ICLR), 2018._