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Reinforcement Learning in Python

Master the fundamentals of reinforcement learning (RL) to create models that can navigate complex real-world environments and train LLMs.
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Track Description

Reinforcement Learning in Python

Master the fundamentals of reinforcement learning (RL) and discover how to build models to navigate complex environments often found in robotics and video games. If you’re new to reinforcement learning or want to specialize in reinforcement learning as a branch of machine learning, this is an ideal place to start. You’ll start by learning about core reinforcement learning concepts, such as Markov decision processes, exploration/exploitation tradeoffs, and dynamic programming algorithms. You'll learn to apply Q-learning, SARSA, and other methods to navigate mountain ranges and frozen lakes from the Gymnasium library. You’ll merge deep learning and reinforcement learning and discover deep reinforcement learning, which can be used to train agents to navigate highly complex environments with little supervision. Along the way, you'll apply these techniques to tackle real-world projects, including optimizing taxi routes and stock trading simulation. With these reinforcement learning tools at hand, you're ready to begin tackling an exciting new application for reinforcement learning: reinforcement learning from human feedback (RLHF). RLHF can be used to improve LLM outputs by training on human feedback to its responses. Start your reinforcement learning journey today!

Prerequisites

There are no prerequisites for this track

  • Course

    1

    Reinforcement Learning with Gymnasium in Python

    Start your reinforcement learning journey! Learn how agents can learn to solve environments through interactions.

  • Project

    bonus

    Taxi Route Optimization with Reinforcement Learning

    Solve the Taxi-v3 environment using Q-learning, ensuring efficient AI-driven transportation.

Reinforcement Learning in Python
3 courses
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