Learning Autonomous Driving Behaviors with LLMs and RL

  • BeginnerLevel

  • 4252+Students Enrolled

  • 1 HrDuration

  • 5Average Rating

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About this Course

  • Learn to train RL agents for autonomous driving using Deep Q-Networks (DQN) and experience replay.
  • Explore reward system design and safety mechanisms for AI-driven navigation in real-world environments.
  • Integrate LLMs to enhance decision-making, improving interpretability and adaptability in driving scenarios.

Learning Outcomes

Autonomous RL Training

Train the RL agents for safe, the human-like driving.

LLM-Enhanced Decisions

Improve the AI interpretability with language models.

Reward Function Design

Create effective incentives for real-world navigation.

Who Should Enroll

  • Professionals and students eager to explore AI, autonomous systems, and ML applications in real-world scenarios.
  • Engineers and developers looking to integrate RL and LLMs into autonomous driving projects more efficient AI systems.
  • AI enthusiasts and researchers interested in cutting-edge technologies and role in safety-critical decision-making.

Course Curriculum

Explore a comprehensive curriculum covering Python, machine learning models, deep learning techniques, and AI applications.

tools

  1. 1. Introduction

  2. 2. Evolution RL

  3. 3. Understanding RL

  4. 4. Challenges with RL

  5. 5. Approach to the Problem Statement

  6. 6. Hands-On: Learning Autonomous Driving Behaviors with LLMs & RL

Meet the instructor

Our instructor and mentors carry years of experience in data industry

company logo
Mayank Baranwal

Senior Scientist, TCS Research

Mayank Baranwal, Senior Scientist at TCS Research and Adjunct Faculty at IIT Bombay, specializes in optimization, control, and network systems. He holds a PhD from UIUC and has received accolades like the Young Scientist Award (2022).

Get this Course Now

With this course you’ll get

  • 1 Hour

    Duration

  • Mayank Baranwal

    Instructor

  • Beginner

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Globally recognized certificate
  • Verifiable online credential
  • Enhances professional credibility
certificate

Frequently Asked Questions

Looking for answers to other questions?

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.

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

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.

rior experience with AI or RL would be beneficial

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.

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