TD 1 Reinforcement Learning

Project Overview

This project is part of the TD 1 Reinforcement Learning assignment. It includes implementations of reinforcement learning algorithms to solve the CartPole-v1 environment and train a robotic arm using Panda-gym.

Environment Setup

Python Virtual Environment

To set up a Python virtual environment, use the following commands:

python -m venv venv source venv/bin/activate # On macOS/Linux venv\Scripts\activate # On Windows

bash

Required Libraries

Install the necessary libraries with the following command:

pip install gymnasium stable-baselines3 wandb panda-gym torch matplotlib

csharp

Project Structure

The project is organized as follows:

. ├── README.md ├── a2c_sb3_cartpole.py ├── a2c_sb3_panda_reach.py ├── evaluate_reinforce_cartpole.py ├── reinforce_cartpole.py ├── reward_plot.png ├── script_hub.py ├── test.py ├── training_wandb.py ├── venv └── wandb

Experiment Tracking

Weights & Biases

• CartPole Experiment: The link to the Weights & Biases run is not available due to the unavailability of the sharing feature.
• Panda Reach Experiment: The link to the Weights & Biases run is not available due to the unavailability of the sharing feature.

Trained Models

Hugging Face Hub

• CartPole Model: Hugging Face Hub - CartPole Model
• Panda Reach Model: Hugging Face Hub - Panda Reach Model

Usage

Running the CartPole Experiment

To run the CartPole experiment, use the following command:

python a2c_sb3_cartpole.py

bash

Running the Panda Reach Experiment

To run the Panda Reach experiment, use the following command:

python a2c_sb3_panda_reach.py

yaml

Evaluating the CartPole Model

To evaluate the CartPole model, use the following command:

python evaluate_reinforce_cartpole.py

shell

Results

Reward Plot

The reward plot is shown below:

License

This project is licensed under the MIT License.