https://github.com/bryanoliveira/soccer-twos-starter
Example training/testing scripts for our soccer-twos-env.
https://github.com/bryanoliveira/soccer-twos-starter
python reinforcement-learning
Last synced: about 1 month ago
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Example training/testing scripts for our soccer-twos-env.
- Host: GitHub
- URL: https://github.com/bryanoliveira/soccer-twos-starter
- Owner: bryanoliveira
- Created: 2021-09-14T22:13:04.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2023-04-12T15:12:13.000Z (over 2 years ago)
- Last Synced: 2025-04-08T15:52:37.330Z (6 months ago)
- Topics: python, reinforcement-learning
- Language: Python
- Homepage:
- Size: 43 KB
- Stars: 0
- Watchers: 1
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Soccer-Twos Starter Kit
Example training/testing scripts for our [Soccer-Twos](https://github.com/bryanoliveira/soccer-twos-env) environment.
## Requirements
- Python 3.8
- See [requirements.txt](requirements.txt)
## Usage
- Clone this repository
- Install the requirements: `pip install -r requirements.txt`
- Run `python example_random.py` to watch a random agent play the game
- Run `python example_ray_team_vs_random.py` to train team vs team against a random agent using [Ray RLLib](https://docs.ray.io/en/latest/rllib.html)
You may also run this environment [on Colab](https://colab.research.google.com/drive/1awcOdo8RU9UdaSRKuqUjvaOTF2O17-os?usp=sharing).
## Agent Packaging
To submit an agent for a Soccer-Twos competition you must follow this instructions:
- Implement a class that inherits from `soccer_twos.AgentInterface` and implements an `act` method
- Fill in your agent's information in the `README.md` file (agent name, authors & emails, and description)
- Test your agent module as described in the next section
- Compress your agent's module folder as `.zip`.
See `example_player_agent/` or `example_team_agent/` module for reference.
## Testing/Evaluating
Use the environment's rollout tool to test your module before submission:
`python -m soccer_twos.watch -m example_player_agent`
You may also run your agent against our [pre-trained baseline (download)](https://drive.google.com/file/d/1WEjr48D7QG9uVy1tf4GJAZTpimHtINzE/view?usp=sharing). Extract the `ceia_baseline_agent` folder to this project's folder and run:
`python -m soccer_twos.watch -m1 example_player_agent -m2 ceia_baseline_agent`