• 1 Hour

  • 4.7/5

  • Intermediate

Course Description

This course dives into the development of autonomous driving behaviors using Reinforcement Learning (RL) and Large Language Models (LLMs). You’ll explore how RL agents are trained to navigate complex, real-world environments while making safe, human-like driving decisions. The course tackles key challenges such as designing effective reward systems, ensuring safety in high-speed driving scenarios, and improving the interpretability of AI decisions. Through practical projects, you will design RL agents using techniques like Deep Q-Networks (DQN), experience replay, and integrate LLMs to enhance decision-making.

Course curriculum

  • 1
    Learning Autonomous Driving Behaviors with LLMs & RL
    • Introduction
    • Evolution RL
    • Understanding RL
    • Challenges with RL
    • Approach to the Problem Statement
    • Hands-On: Learning Autonomous Driving Behaviors with LLMs & RL

Certificate of Completion

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Certificate of Completion

Who should Enroll?

  • Professionals and students interested in AI, autonomous systems, and machine learning.

  • Engineers and developers looking to apply RL and LLMs in real-world autonomous driving projects.

  • Anyone seeking to explore cutting-edge AI technologies and their applications in safety-critical systems.

About the Instructor

Mayank Baranwal - Senior Scientist at TCS Research | Adjunct Professor at IITB | INAE Young Associate | UIUC | IITK

Mayank Baranwal is a Senior Scientist at Tata Consultancy Services (TCS) Research, Mumbai, and an Adjunct Faculty at IIT Bombay. He holds a PhD in Mechanical Science and Engineering from the University of Illinois, Urbana-Champaign (UIUC). His research focuses on optimization, control, and network systems with applications in supply chains, power networks, and deep learning. He has received several accolades, including the Young Scientist Award (2022) and the Gold Award for Best Smart Technology in Electricity Transmission (2023).
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About the Instructor

FAQs

  • What is the focus of this course?

    This course focuses on using Reinforcement Learning (RL) and Large Language Models (LLMs) to train autonomous driving systems. You'll learn how to develop RL agents that make safe, human-like driving decisions in complex environments like highways.

  • How are LLMs used in this course?

    LLMs are integrated to guide RL agents by enhancing their decision-making capabilities, particularly in designing reward systems that align with human behavior.

  • What practical skills will I gain?

    You will gain hands-on experience in training RL agents using techniques like Deep Q-Networks (DQN) and experience replay. Additionally, you'll learn how to design reward systems and apply them to real-world autonomous driving scenarios.

  • Is prior experience with RL or AI required?

    Prior experience with AI or RL would be beneficial.

  • What challenges in autonomous driving are addressed?

    The course tackles key challenges such as reward hacking, ensuring safety in high-speed driving environments, and the "black box" nature of AI decisions. You will learn strategies to overcome these issues using RL and LLMs.

Key Takeaways

  • Learn how to train RL agents for safe, human-like autonomous driving behavior.

  • Understand how LLMs enhance decision-making and interpretability in AI systems.

  • Gain practical experience in designing and applying reward functions for real-world autonomous environments.