assignment2 omar

Introduction

The project features a trained Proximal Policy Optimization (PPO) agent playing LunarLander-v2, utilizing the stable-baselines3 library. This model is a practical implementation of deep reinforcement learning techniques.

Architecture

The model architecture is based on the PPO algorithm, which is a popular choice for reinforcement learning tasks due to its balance of exploration and exploitation. The environment used for training is LunarLander-v2, a standard OpenAI Gym environment that simulates a lunar landing task.

Training

The model was trained using stable-baselines3, an implementation of reinforcement learning algorithms in Python. Training involved optimizing the agent's policy to maximize its reward in the LunarLander-v2 environment. The mean reward achieved by the agent is 10 with a standard deviation of 7.11, indicating the agent's performance across different episodes.

Guide: Running Locally

1. Install Dependencies: Ensure that Python and the stable-baselines3 library are installed. You can install the library using pip:

   pip install stable-baselines3
   

2. Set Up Environment: Install the OpenAI Gym environment for LunarLander-v2:

   pip install gym
   

3. Load the Model: Download the trained PPO agent model. (Note: Specific download instructions or a link should be provided.)

4. Run the Model: Use the stable-baselines3 library to load and run the agent in the LunarLander-v2 environment. (Note: Detailed code snippets should be provided here.)

5. Cloud GPUs: For enhanced performance, especially during training, consider using cloud GPU services such as AWS EC2, Google Cloud, or Azure.

License

The project is distributed under a license that should be specified. Ensure to review the license terms for permissions and limitations regarding the use of the model and code.