Deep Reinforcement Learning for Self-Driving Robots

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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
Included with a Manning Online subscription
catalog / Data Science / Machine Learning / Computer Vision
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team

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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.
Manning author Hans Gunnoo shares what he likes about the Manning liveProject platform.

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

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

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