Скачать или смотреть Build a 3D Robotic Arm Simulator in Python |Reinforcement Learning & Analytical Inverse Kinematics

Dive into the world of robotics and artificial intelligence as we build and compare two powerful methods for controlling a 3D robotic arm in a simulated environment! In this video, we explore both a classical, mathematical approach and a modern, machine learning-based solution.

*What you'll see in this video:*

*Analytical Inverse Kinematics (IK):* Watch a robotic arm use a precise, direct mathematical solver to instantly calculate the joint angles needed to reach a moving target. See how it handles complex movements and singularities with ease.
*Deep Reinforcement Learning (DQN):* Witness a Deep Q-Network (DQN) agent learn from scratch how to control the same robotic arm. The agent learns through trial and error, getting a reward for getting closer to the target and a penalty for moving away.
*Head-to-Head Comparison:* We'll compare the performance, strengths, and weaknesses of both methods, highlighting the difference between a model-based and a data-driven approach.

This project is a perfect example of how different engineering and AI paradigms can be applied to the same problem. Whether you're a robotics enthusiast, a machine learning student, or a Python developer, you'll find something valuable here.

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*Project Details & Code*

The full source code for this project is available on GitHub. Feel free to clone the repository, run the simulations, and experiment with the code yourself!

*Code is available here:* https:// github.com/rezaxr14/3D-Robot-Control-RL-Analytical.git

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*Technologies Used*

*Python 3:* The core language for the entire project.
*PyTorch:* The deep learning framework used to build and train the DQN agent.
*Gymnasium:* Provides the standardized environment for the reinforcement learning agent.
*NumPy:* For all numerical and mathematical computations.
*Matplotlib:* For the real-time 3D visualization of the robotic arm.

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*How to Run the Code*

1. *Clone the repository:*
```bash
git clone https:// github.com/rezaxr14/3D-Robot-Control-RL-Analytical.git
cd 3D-Robot-Control-RL-Analytical
```
2. *Install dependencies:*
```bash
pip install -r requirements.txt
```
3. *Run the Inverse Kinematics demo:*
```bash
cd Analytical_Inverse_Kinematics
python main.py
```
4. *Train the RL agent:*
```bash
cd RL_Double_Dueling_DQN
python train.py
```
5. *Test the trained RL agent:*
```bash
cd RL_Double_Dueling_DQN
python test.py
```

Thanks for watching! If you enjoyed this video, please like, subscribe, and share it with others. Let me know in the comments which method you found more interesting!