This repository contains implementations of various Reinforcement Learning algorithms and environments. It serves as a centralized hub for exploring and understanding different approaches to solving complex decision-making problems.
- Clone this repository
git clone https://github.com/A-A7med-i/RL.git- Navigate to the project directory:
cd reinforcement-learning- Q-Learning: A classic off-policy reinforcement learning algorithm that learns an action-value function.
- SARSA: An on-policy reinforcement learning algorithm that learns an action-value function using an on-policy behavior policy.
- Deep Q-Networks (DQN): A deep learning-based approach to reinforcement learning that addresses the problem of overestimation bias.
- Policy Gradient Methods: A family of algorithms that directly learn a policy function to map states to actions.
- Actor-Critic Methods: A class of algorithms that combine both policy-based and value-based approaches.
- CartPole-v1: A simple environment where the goal is to balance a pole on a cart.
- MountainCar-v0: A more challenging environment where the goal is to drive a car up a hill.
- LunarLander-v2: A more complex environment where the goal is to land a lunar lander safely on a landing pad.
- OpenAI Gym: A toolkit for developing and comparing reinforcement learning algorithms.
- Custom Environments: [Describe any custom environments you've created or plan to create]
Contributions are welcome! Please follow these guidelines:
- Fork the repository.
- Create a new branch for your feature or bug fix.
- Make your changes and commit them.
- Push your changes to your fork.
- Submit a pull request to the main repository.
This repository is licensed under the MIT License. See the LICENSE file for more details MIT License.