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PROJECT
Train an Agent to Self-Drive a Taxi using Reinforcement Learning
In this project, we’ll train an agent to pick up passengers and drop them at their destination in the fastest way possible using reinforcement learning algorithms built from scratch. We’ll use the Gymnasium library to simulate the environment and Matplotlib to plot the learning curves.
You will learn to:
Perform reinforcement learning.
Work with Gymnasium environments (formerly OpenAI Gym).
Train and test a Q-learning agent.
Train and test a SARSA agent.
Skills
Machine Learning
Robotics
Reinforcement Learning
Autonomous Vehicles
Prerequisites
Programming in Python
Basic knowledge of machine learning
Technologies
Python
Gymnasium
Matplotlib
Project Description
In this project, we’ll use reinforcement learning to train an agent to become a taxi driver whose job is to pick up passengers and bring them to their destination in the fastest way possible.
We’ll first learn how to work with the Gymnasium API (formerly Open AI Gym), which is a standard API for developing and comparing reinforcement learning algorithms, and explore the properties of the Taxi environment.
Then, we’ll implement two popular reinforcement learning algorithms: Q-learning, which is an off-policy algorithm, and SARSA, which is an on-policy algorithm. We’ll implement both algorithms from scratch in Python and then use them to train and test the taxi driver.
Finally, we’ll plot the learning curves of the algorithms and compare their performance.
Project Tasks
1
Getting Started
Task 0: Introduction
2
The Taxi Environment
Task 1: Explore The Taxi Environment
Task 2: Implement a Basic Agent-Environment Interaction Loop
3
Build the Q-Learning Agent
Task 3: Implement the Q-Learning Algorithm
Task 4: Train the Q-Learning Agent
Task 5: Fine-Tune the Hyperparameters
Task 6: Display the Learning Curve
Task 7: Show the Learned Policy in Action
4
Build the SARSA Agent
Task 8: Implement the SARSA Algorithm
Task 9: Train and Test the SARSA Agent
Congratulations!
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.