Welcome to the ML-Agents Reinforcement Learning project! This project utilizes Unity's ML-Agents toolkit in conjunction with OpenAI's Gym to explore reinforcement learning algorithms in a simulated environment.
This repository contains code and resources to set up an environment where reinforcement learning agents can be trained using Unity's ML-Agents framework and interface with OpenAI's Gym toolkit. The goal is to create and train intelligent agents capable of learning and performing tasks within a controlled environment.
- Integration of Unity's ML-Agents and OpenAI's Gym for reinforcement learning experiments.
- Customizable environments and scenarios for training agents.
- Support for various reinforcement learning algorithms.
- Visualization tools to track agent performance and learning progress.
To run this project, ensure you have the following installed:
- Unity
- Python
- ML-Agents
- OpenAI Gym
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Clone this repository:
git clone https://github.com/your_username/ml-agents-reinforcement-learning.git
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Install Unity and necessary packages following the instructions provided by Unity and ML-Agents documentation.
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Install Python and required dependencies using
pip:pip install gym # Other required packages
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Open the Unity project in the Unity Editor.
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Set up the environment, agents, and tasks within Unity's ML-Agents framework.
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Run the Python scripts to interface with the Unity environment through OpenAI's Gym interface and start the training process:
python train.py
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Monitor the training progress and visualize agent performance using the provided tools.
Contributions are welcome! If you want to contribute to this project, feel free to submit pull requests or open issues for bug fixes, new features, or improvements.
This project is licensed under the MIT License - see the LICENSE file for details.
Special thanks to Unity Technologies and OpenAI for their incredible frameworks and tools that make this project possible.