| Name | gama-gymnasium JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | A Gymnasium environment for reinforcement learning with GAMA agent-based simulations |
| upload_time | 2025-07-17 04:09:06 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License
Copyright (c) 2024 IRD - INRAE
Permission is hereby granted, free of charge, to any person obtaining a copy
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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| keywords |
agent-based-modeling
gama
gymnasium
reinforcement-learning
rl
simulation
|
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# GAMA-Gymnasium
[](https://pypi.org/project/gama-gymnasium/)
[](LICENSE)
**GAMA-Gymnasium** is a generic [Gymnasium](https://gymnasium.farama.org/) environment that enables the integration of simulations from the [GAMA](https://gama-platform.org/) modeling platform with reinforcement learning algorithms.
## ๐ฏ Purpose
This library allows researchers and developers to easily use GAMA models as reinforcement learning environments, leveraging the power of GAMA for agent-based modeling and the Python ecosystem for AI.
## โก Quick Start
### Installation
```bash
pip install gama-gymnasium
```
### Prerequisites
- **GAMA Platform**: Install GAMA from [gama-platform.org](https://gama-platform.org/download)
- **Python 3.8+** with required dependencies
```bash
pip install gama-client gymnasium
```
### Basic Usage
```python
import gama_gymnasium
import gymnasium as gym
# Create the environment
env = gym.make('gama-gymnasium-v0',
gama_model_path='your_model.gaml',
gama_port=6868)
# Use as a standard Gymnasium environment
obs, info = env.reset()
for _ in range(100):
action = env.action_space.sample()
obs, reward, terminated, truncated, info = env.step(action)
if terminated or truncated:
obs, info = env.reset()
```
### GymansiumLink Skill
The `GymansiumLink` skill is a GAMA skill of the `gama_gymnasium` plugin that allows GAMA agents to interact with Gymnasium environments. It adds 8 varriables and 1 action to the agent
Structure of the skill:
```gaml
species GymAgent skills:[GymnasiumLink]{
map action_space;
map observation_space
unknown state;
float reward;
boolean terminated;
boolean truncated;
map info;
unknown next_action;
action update_data;
}
```
### GAMA Configuration
1. **Add the GAMA component** to your model:
Make sure you have the GAMA plugin gama_gymnasium and add a species `GymAgent` with the skill `GymnasiumLink` to your model:
```gaml
species GymAgent skills:[GymnasiumLink];
```
Set up the `action_space` and `observation_space`:
```gaml
global {
init{
create GymAgent;
GymAgent[0].action_space <- ["type"::"Discrete", "n"::4];
GymAgent[0].observation_space <- ["type"::"Box", "low"::0, "high"::grid_size, "shape"::[2], "dtype"::"int"];
}
}
```
Update the gym agent's data after the action is completed:
```gaml
ask GymAgent[0] {
do update_data;
}
```
2. **Launch GAMA in server mode**:
```bash
# Linux/MacOS
./gama-headless.sh -socket 6868
# Windows
gama-headless.bat -socket 6868
```
## ๐ Project Structure
```text
gama-gymnasium/
โโโ ๐ src/ # Main Python package source code
โโโ ๐ tests/ # Comprehensive test suite
โโโ ๐ examples/ # Complete examples and tutorials
โโโ pyproject.toml # Python package configuration
โโโ LICENSE # Package license
โโโ ๏ฟฝ pytest.ini # Testing configuration
```
## ๐ Documentation and Examples
### ๐ Tutorials and Examples
| Example | Description | Documentation |
| -------------------------------- | ----------------------------------------------------- | ------------------------------------------------------ |
| **Basic Example** | Introduction to GAMA-Gymnasium integration | [๐ README](examples/basic_example/README.md) |
| **CartPole DQN** | Deep Q-Network implementation on CartPole environment | [๐ README](examples/cartpole%20DQN/README.md) |
| **Frozen Lake Q-Learning** | Q-Learning on Frozen Lake environment | [๐ README](examples/frozen%20lake%20QLearning/README.md) |
### ๐ Detailed Guides
- **[Basic Example Guide](examples/basic_example/README.md)**: Complete tutorial for creating your first environment
- **[Direct GAMA Test](examples/basic_example/README_basic_test.md)**: Low-level communication with GAMA
- **[Source Code Documentation](src/README.md)**: Technical documentation of the package structure
- **[Testing Guide](tests/README.md)**: Comprehensive testing framework and best practices
## ๐ Advanced Installation
### From Source Code
```bash
git clone https://github.com/gama-platform/gama-gymnasium.git
cd gama-gymnasium
pip install -e src/
```
## ๐งช Testing and Validation
```bash
# Run tests
python tests/test_manager.py --quick
```
## ๐ค Contributing
Contributions are welcome! Check the [issues](https://github.com/gama-platform/gama-gymnasium/issues) to see how you can help.
## ๐ Useful Links
- [GAMA Platform](https://gama-platform.org/)
- [Gymnasium Documentation](https://gymnasium.farama.org/)
- [GAMA-Client PyPI](https://pypi.org/project/gama-client/)
---
For more technical details and practical examples, check the documentation in the [`examples/`](examples/) and [`src/`](src/) folders, or explore our comprehensive [testing framework](tests/README.md).
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"description": "# GAMA-Gymnasium\n\n[](https://pypi.org/project/gama-gymnasium/)\n[](LICENSE)\n\n**GAMA-Gymnasium** is a generic [Gymnasium](https://gymnasium.farama.org/) environment that enables the integration of simulations from the [GAMA](https://gama-platform.org/) modeling platform with reinforcement learning algorithms.\n\n## \ud83c\udfaf Purpose\n\nThis library allows researchers and developers to easily use GAMA models as reinforcement learning environments, leveraging the power of GAMA for agent-based modeling and the Python ecosystem for AI.\n\n## \u26a1 Quick Start\n\n### Installation\n\n```bash\npip install gama-gymnasium\n```\n\n### Prerequisites\n\n- **GAMA Platform**: Install GAMA from [gama-platform.org](https://gama-platform.org/download)\n- **Python 3.8+** with required dependencies\n\n```bash\npip install gama-client gymnasium\n```\n\n### Basic Usage\n\n```python\nimport gama_gymnasium\nimport gymnasium as gym\n\n# Create the environment\nenv = gym.make('gama-gymnasium-v0', \n gama_model_path='your_model.gaml',\n gama_port=6868)\n\n# Use as a standard Gymnasium environment\nobs, info = env.reset()\nfor _ in range(100):\n action = env.action_space.sample()\n obs, reward, terminated, truncated, info = env.step(action)\n if terminated or truncated:\n obs, info = env.reset()\n```\n\n### GymansiumLink Skill\n\nThe `GymansiumLink` skill is a GAMA skill of the `gama_gymnasium` plugin that allows GAMA agents to interact with Gymnasium environments. It adds 8 varriables and 1 action to the agent\n\nStructure of the skill:\n\n```gaml\nspecies GymAgent skills:[GymnasiumLink]{\n map action_space;\n map observation_space\n\n unknown state;\n float reward;\n boolean terminated;\n boolean truncated;\n map info;\n\n unknown next_action;\n\n action update_data;\n}\n```\n\n### GAMA Configuration\n\n1. **Add the GAMA component** to your model:\n Make sure you have the GAMA plugin gama_gymnasium and add a species `GymAgent` with the skill `GymnasiumLink` to your model:\n\n ```gaml\n species GymAgent skills:[GymnasiumLink];\n ```\n\n Set up the `action_space` and `observation_space`:\n\n ```gaml\n global {\n init{\n create GymAgent;\n GymAgent[0].action_space <- [\"type\"::\"Discrete\", \"n\"::4];\n GymAgent[0].observation_space <- [\"type\"::\"Box\", \"low\"::0, \"high\"::grid_size, \"shape\"::[2], \"dtype\"::\"int\"];\n }\n }\n ```\n\n Update the gym agent's data after the action is completed:\n\n ```gaml\n ask GymAgent[0] {\n do update_data;\n }\n ```\n2. **Launch GAMA in server mode**:\n\n```bash\n# Linux/MacOS\n./gama-headless.sh -socket 6868\n\n# Windows\ngama-headless.bat -socket 6868\n```\n\n## \ud83d\udcc1 Project Structure\n\n```text\ngama-gymnasium/\n\u251c\u2500\u2500 \ud83d\udcc1 src/ # Main Python package source code\n\u251c\u2500\u2500 \ud83d\udcc1 tests/ # Comprehensive test suite\n\u251c\u2500\u2500 \ud83d\udcc1 examples/ # Complete examples and tutorials\n\u251c\u2500\u2500 pyproject.toml\t # Python package configuration\n\u251c\u2500\u2500 LICENSE # Package license\n\u2514\u2500\u2500 \ufffd pytest.ini # Testing configuration\n```\n\n## \ud83d\udcda Documentation and Examples\n\n### \ud83d\ude80 Tutorials and Examples\n\n| Example | Description | Documentation |\n| -------------------------------- | ----------------------------------------------------- | ------------------------------------------------------ |\n| **Basic Example** | Introduction to GAMA-Gymnasium integration | [\ud83d\udcd6 README](examples/basic_example/README.md) |\n| **CartPole DQN** | Deep Q-Network implementation on CartPole environment | [\ud83d\udcd6 README](examples/cartpole%20DQN/README.md) |\n| **Frozen Lake Q-Learning** | Q-Learning on Frozen Lake environment | [\ud83d\udcd6 README](examples/frozen%20lake%20QLearning/README.md) |\n\n### \ud83d\udcd6 Detailed Guides\n\n- **[Basic Example Guide](examples/basic_example/README.md)**: Complete tutorial for creating your first environment\n- **[Direct GAMA Test](examples/basic_example/README_basic_test.md)**: Low-level communication with GAMA\n- **[Source Code Documentation](src/README.md)**: Technical documentation of the package structure\n- **[Testing Guide](tests/README.md)**: Comprehensive testing framework and best practices\n\n## \ud83d\udee0 Advanced Installation\n\n### From Source Code\n\n```bash\ngit clone https://github.com/gama-platform/gama-gymnasium.git\ncd gama-gymnasium\npip install -e src/ \n```\n\n## \ud83e\uddea Testing and Validation\n\n```bash\n# Run tests\npython tests/test_manager.py --quick\n```\n\n## \ud83e\udd1d Contributing\n\nContributions are welcome! Check the [issues](https://github.com/gama-platform/gama-gymnasium/issues) to see how you can help.\n\n## \ud83d\udd17 Useful Links\n\n- [GAMA Platform](https://gama-platform.org/)\n- [Gymnasium Documentation](https://gymnasium.farama.org/)\n- [GAMA-Client PyPI](https://pypi.org/project/gama-client/)\n\n---\n\nFor more technical details and practical examples, check the documentation in the [`examples/`](examples/) and [`src/`](src/) folders, or explore our comprehensive [testing framework](tests/README.md).\n",
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