Reinforcement Learning for Self-driving Vehicles

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prerequisites
basic Python • basic Keras • basics of reinforcement learning
skills learned
working with OpenAI Gym • developing reward-shaped training agents • using rl-agent and stable-baselines packages to directly train models
Ashish Rana
3 weeks · 3-5 hours per week · INTERMEDIATE
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Look inside
In this liveProject, you’ll develop an AI driving agent that can simulate independent driving. Your agent should be able to handle changing lanes, navigating roundabouts, and even self-parking. In order to create this complex agent, you’ll use powerful reinforcement learning techniques including model-based learning, model-free learning, and simulation-search/planning algorithms. Go hands-on to train your AI in AI Gym on Google Colab, improve your model’s performance, and provide professional-level documentation of your work for your colleagues.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Ashish Rana
Ashish Rana is an experienced professional in the data engineering and predictive analytics space. He has worked developing ingestion and predictive analytics pipelines on machine and business data use-cases. He also carries out academic research and has authored papers on machine learning, deep learning, and reinforcement learning. He is currently researching on topics like reinforcement learning application use-cases and argument mining for natural language inference tasks.

prerequisites

This liveProject is for data scientists familiar with programming basic algorithm implementations in Python and the basics of machine learning. To begin this liveProject you will need to be familiar with:

TOOLS
  • Basic Python
  • Basic Keras
  • Basic Matplotlib
  • Basic Jupyter Notebook
TECHNIQUES
  • Basics of machine learning
  • Basics of reinforcement learning

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