This project demonstrates the application of various reinforcement learning (RL) algorithms to solve the Lunar Lander problem, a classic control problem provided by OpenAI's Gym environment.
- In this project, we train an agent to successfully land a lunar module on a designated landing pad using reinforcement learning.
- The goal is to achieve a soft landing, minimizing the impact velocity and landing within the designated area.
- The agent receives rewards based on its actions, such as maintaining a proper orientation, landing softly, and avoiding crashing.
The project includes implementations of the following RL algorithms:
- Deep Q-Network (DQN)
- Double DQN
- Dueling DQN
- Policy Gradient Methods (REINFORCE)
- Actor-Critic Methods (A2C, A3C)
- Proximal Policy Optimization (PPO)
Ensure you have the following installed:
- Python 3.7+
- pip (Python package installer)
- Git
The results of the training, including the performance metrics and trained models, are saved in the logs/ and models/ directories, respectively. You can visualize the training progress using TensorBoard:
This project uses the following open-source libraries:
- OpenAI Gym
- PyTorch
- NumPy
- Matplotlib
Thank you for checking out this project! If you have any questions or feedback, feel free to open an issue or reach out.
