Deep Reinforcement Learning for Self-Driving Robots you own this product

prerequisites
intermediate Python • basics of NumPy • basics of Matplotlib • intermediate PyTorch
skills learned
implementing Q-learning • improving algorithm performance with deep reinforcement learning • creating a reinforcement learning environment in OpenAI Gym
Hans Gunnoo and Byron Galbraith
5 weeks · 4-6 hours per week · INTERMEDIATE
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liveProject liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $32.49 $49.99 you save $17 (35%)
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This live project does a very good job guiding the learner in the reinforcement learning topic. Having the opportunity to play with OpenAI gave me a good basic understanding of how this approach to machine learning works. Someone who is looking to learn reinforcement learning will definitely find this beneficial.

Ariel Gamino, Senior Software Development Engineer, GLG (Gerson Lehrman Group)
Look inside
In this liveProject, you’ll investigate reinforcement learning approaches that will allow autonomous robotic carts to navigate a warehouse floor without any bumps and crashes. Your challenges will include teaching warehouse navigation with tabular Q-learning, leveraging neural networks and Deep Q-Network models to avoid collisions, and putting together a simulation environment to handle custom reinforcement learning problems.

Please note: This project recommends using Colab Pro, which is an additional cost outside this project.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

book and video resources

When you start your liveProject, you get full access to the following books and videos for 90 days.

project authors

Hans Gunnoo
Hans Gunnoo is an AI engineer at Deloitte Digital, London, where he works as part of a rapid prototyping team exploring state-of-the-art technologies. He experiments daily with cutting-edge technology, including deepfakes, voice cloning, and location tracking. Hans holds a masters in electronic engineering with artificial intelligence.
Byron Galbraith
Byron Galbraith is the Chief Technology Officer and co-founder of Talla, where he has worked on translating advancements in machine learning and natural language processing to build cutting edge knowledge automation solutions. Byron has a PhD in Cognitive and Neural Systems from Boston University and an MS in Bioinformatics from Marquette University. His research expertise includes natural language processing, reinforcement learning, brain-computer interfaces, neuromorphic robotics, spiking neural networks, and high-performance computing. Byron has held several software engineering roles including back-end system engineer, full stack web developer, office automation consultant, and game engine developer at companies ranging in size from a two-person startup to a multinational enterprise.

prerequisites

This liveProject is for intermediate Python programmers familiar with deep learning and object-oriented programming. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Basics of Jupyter Notebook
  • Basics of NumPy
  • Basics of Matplotlib
  • Intermediate PyTorch

you will learn

In this liveProject, you’ll learn how to identify problems that can be solved with reinforcement learning and how to implement RL solutions. As one of the three fundamental machine learning paradigms, reinforcement learning is an essential part of any machine learning resume.

  • Interacting with a pre-built OpenAI environment
  • Working with matrices and agent exploration using NumPy
  • Implementing Q-learning
  • Visualizing performance evolution with Matplotlib
  • Incorporating deep learning to replace the Q-table with a neural network
  • Using deep reinforcement learning techniques to improve algorithm performance
  • Create your own reinforcement learning environment

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

A genuinely interesting project. The scenario makes sense, relates to the real world and is interesting. It is good material, relevant and really interesting.

Alex Lucas, Director, Security, Amazon Web Services

All in all, a very satisfying liveProject. Reinforcement Learning is very useful in simulating environments, sophisticated AI, robotics, and could be key to bring AI closer to AGI [Artificial General Intelligence].

Sruti Shivakumar
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