Course details

Reinforcement Learning: Tools & Frameworks

Reinforcement Learning: Tools & Frameworks


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

This 9-video course explores how to implement machine learning reinforcement learning by examining the terminology, including agents, the environment, state, and policy. This course demonstrates how to implement reinforcement learning by using Keras and Python; how to ensure that you can build a model; and how to launch and use Ubuntu, and VI editor to do score calculations. First, learn the role of the Markov decision process in which the agent observes the environment, with output consisting of a reward and the next state, and then acts upon it. You will explore Q-learning, a model-free reinforcement learning technique, an asynchronous dynamic programming approach, and will learn about the Q-learning rule, and Deep Q-learning. Next, learn the steps to install TensorFlow for reinforcement learning, as well as framework, which is used for reinforcement learning provided by OpenAI. Then learn how to implement TensorFlow for reinforcement learning. Finally, you will learn to implement Q-learning using Python, and then utilize capabilities of OpenAl Gym and FrozenLake.



Expected Duration (hours)
0.6

Lesson Objectives

Reinforcement Learning: Tools & Frameworks

  • Course Overview
  • recognize the different types of reinforcement learning that can be implemented for decision-making
  • implement reinforcement learning using Keras and Python
  • identify the role of the Markov decision process in reinforcement learning
  • describe Q-learning, Q-learning rule, and deep Q-learning
  • install TensorFlow
  • implement reinforcement learning using TensorFlow
  • implement Q-learning using Python
  • implement reinforcement learning using Python and TensorFlow and implement Q-learning using Python
  • Course Number:
    it_mlrlfndj_02_enus

    Expertise Level
    Intermediate