README.md

metadata

language: en
license: mit
tags:
  - reinforcement-learning
  - q-learning
  - gridworld
  - mountain-car
  - openai-gym
datasets:
  - gym

Q-Learning in Reinforcement Learning Environments

Project Overview

This project implements the Q-Learning algorithm in two different reinforcement learning environments: a custom-built Gridworld and the MountainCar-v0 environment from the OpenAI Gym library.

Part 1: Custom Gridworld Environment

A simple 5x5 Gridworld where the goal is to navigate from the start state to the goal state, avoiding obstacles and minimizing the number of steps:

• Grid Layout:
  • Start at (0, 0)
  • Goal at (4, 4)
  • Obstacles at (2, 2) and (3, 3)
• Rewards:
  • Goal: +100
  • Obstacle: -10
  • Each step: -1

Part 2: MountainCar-v0 Environment

Utilizing the standard MountainCar-v0 environment from the gym library, where the agent must drive a car up a steep hill:

• Environment Features:
  • The agent learns to balance momentum and gravity to reach the peak.
  • Visualize the environment using env.render().

Video Preview

Watch the demonstration video on YouTube.

Implementation Details

• Q-Learning:
  • Initialize and update the Q-table using the Bellman equation.
  • An epsilon-greedy policy is used for action selection.
• Training and Testing:
  • Train the agent over multiple episodes.
  • Visualize and analyze the learned policy to evaluate performance.

How to Use

1. Requirements: Ensure you have Python and the necessary libraries installed, including gym for the MountainCar environment.
2. Setup:
   • Clone the repository: git clone https://huggingface.co/Karim2211/ReinforcementLearningModels/tree/main
   • Install dependencies: pip install -r requirements.txt
3. Running the Code:
   • Navigate to the project directory.
   • For Gridworld: python gridworld.py
   • For MountainCar: python mountain_car.py
4. Loading Models:
   • Models are saved in the models/ directory.
   • To load and test models:

     import pickle
     # For Gridworld model
     with open('models/gridworld_model.pkl', 'rb') as f:
         gridworld_model = pickle.load(f)
     # For MountainCar model
     with open('models/mountain_car_model.pkl', 'rb') as f:
         mountain_car_model = pickle.load(f)
     

5. Testing the Models:
   • Use the loaded models to make predictions or to evaluate the policies.
   • Example testing code:

     # Test Gridworld model
     gridworld.test_policy(gridworld_model)
     # Test MountainCar model
     mountain_car.test_policy(mountain_car_model)
     

Dependencies

• Python 3.x
• gym
• numpy
• matplotlib (optional for visualization)

Authors

• Karim Ashraf
• Shiref Elhalawany
• Mahitab Waleed

License

This project is licensed under the MIT License.