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.
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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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