Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Academic publication links
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○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (11.3%) to scientific vocabulary
Repository
强化学习选修课期末大作业代码
Basic Info
- Host: GitHub
- Owner: Zheng-guangyuan
- Language: Python
- Default Branch: main
- Size: 304 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Final_RL
This repository contains the implementation and experiments for multi-agent reinforcement learning algorithms, including CPPO, IPPO, and MAPPO, tested on the VMAS balance scenario.
File Structure
- docs/: Documentation files for the project.
- vmas/: VMAS environment and simulation files.
- CITATION.cff: Citation file for referencing this project.
- README.md: This README file providing an overview of the project.
- codecov.yml: Configuration file for code coverage tools.
- requirements.txt: List of Python dependencies required for this project.
- setup.cfg: Configuration file for Python packaging.
- setup.py: Script for installing the project as a Python package.
- test_CPPO.py: Implementation and tests for the CPPO algorithm.
- test_IPPO.py: Implementation and tests for the IPPO algorithm.
- test_MAPPO.py: Implementation and tests for the MAPPO algorithm.
- test_PPO.py: Baseline implementation and tests for PPO.
Prerequisites
- Python 3.8 or higher
- Ray 2.1.0
- VMAS environment (included in the repository)
- Additional dependencies listed in
requirements.txt
To install the required dependencies:
bash
pip install -r requirements.txt
Usage
Running the Algorithms
Each of the test scripts (test_CPPO.py, test_IPPO.py, test_MAPPO.py, test_PPO.py) can be executed to train and evaluate the corresponding algorithm in the VMAS balance scenario.
For example, to run the CPPO algorithm:
bash
python test_CPPO.py
Configurations
- Modify the environment and training parameters directly in the respective test scripts.
- Training logs and evaluation results will be displayed in the console or logged via tools like
wandb(if configured).
Environment
The VMAS environment is a vectorized multi-agent simulation framework for reinforcement learning. This repository uses the balance scenario with customizable configurations.
Owner
- Name: 郑广源
- Login: Zheng-guangyuan
- Kind: user
- Location: Sun Yat-sen University/major in Computer Science
- Company: Sun Yat-sen University
- Website: https://www.sysu.edu.cn/sysuen/
- Repositories: 1
- Profile: https://github.com/Zheng-guangyuan
Citation (CITATION.cff)
cff-version: 1.2.0
message: If you use this software, please cite it as below.
title: VMAS
authors:
- family-names: Bettini
given-names: Matteo
preferred-citation:
type: conference-paper
title: "VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning"
authors:
- family-names: Bettini
given-names: Matteo
- family-names: Kortvelesy
given-names: Ryan
- family-names: Blumenkamp
given-names: Jan
- family-names: Prorok
given-names: Amanda
collection-title: Proceedings of the 16th International Symposium on Distributed Autonomous Robotic Systems
publisher: Springer
year: 2023
GitHub Events
Total
- Push event: 2
- Create event: 2
Last Year
- Push event: 2
- Create event: 2
Dependencies
- gym *
- numpy *
- pyglet <=1.5.27
- six *
- torch *
- gym *
- numpy *
- pyglet <=1.5.27
- six *
- torch *
- numpy *