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Home » Technology » AI / Machine Learning » Q-Learning with Python

Course Skill Level:

Foundational

Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:

    QLWPYTL21E09

Who should attend & recommended skills:

Those with Apache knowledge and basic Linux skills

Who should attend & recommended skills

  • This course is geared for those with Apache knowledge who wish to leverage the power of reward-based training for deep learning models with Python.
  • Skill-level: Foundation-level Q-Learning with Python skills for Intermediate skilled team members. This is not a basic class.
  • Linux: Basic (1-2 years’ experience) including familiarity with command-line options such as ls, cd, cp, and su

About this course

Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers. This course starts off by introducing you to reinforcement learning and Q-learning, in addition to helping you become familiar with OpenAI Gym as well as libraries such as Keras and TensorFlow. A few lessons into the course, you will gain insights into model-free Q-learning and use deep Q-networks and double deep Q-networks to solve complex problems. This course will guide you in exploring use cases such as self-driving vehicles and OpenAI Gym CartPole problem. You will also learn how to tune and optimize Q-networks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving real-world problems. You will also explore how to use Q-learning and related algorithms in scientific research. Toward the end, you will gain insight into what is in store for reinforcement learning. By the end of this course, you will be equipped with the skills you need to solve reinforcement learning problems using Q-learning algorithms with OpenAI Gym, Keras, and TensorFlow.

Skills acquired & topics covered

  • Q-learning algorithms to train neural networks using Markov Decision Process (MDP)
  • Practical deep reinforcement learning using Q-Networks
  • State-based unsupervised learning for machine learning models
  • The fundamentals of reinforcement learning and the state-action-reward process
  • Markov Decision Processes
  • Getting well-versed with libraries such as Keras, and TensorFlow
  • Creating and deploying model-free learning and deep Q-learning agents with TensorFlow, Keras, and OpenAI Gym
  • Choosing and optimizing a Q-networks learning parameters and fine-tune its performance
  • Discovering real-world applications and use cases of Q-learning

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