Reinforcement Learning: Tools & Frameworks

Machine Learning    |    Intermediate
  • 9 videos | 34m 26s
  • Includes Assessment
  • Earns a Badge
Rating 4.1 of 8 users Rating 4.1 of 8 users (8)
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.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • 2m 3s
  • 6m 45s
    After completing this video, you will be able to recognize the different types of reinforcement learning that can be implemented for decision-making. FREE ACCESS
  • Locked
    3.  Reinforcement Learning with Keras and Python
    3m 12s
    Find out how to implement reinforcement learning using Keras and Python. FREE ACCESS
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    4.  Markov Decision Process
    2m 54s
    Find out how to identify the role of the Markov decision process in reinforcement learning. FREE ACCESS
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    5.  Q-Learning Concepts
    3m 33s
    After completing this video, you will be able to describe Q-learning, the Q-learning rule, and deep Q-learning. FREE ACCESS
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    6.  TensorFlow Installation
    3m 15s
    In this video, you will learn how to install TensorFlow. FREE ACCESS
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    7.  Reinforcement Learning and TensorFlow
    3m 47s
    In this video, you will learn how to implement reinforcement learning using TensorFlow. FREE ACCESS
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    8.  Q-learning and Python
    4m 58s
    During this video, you will learn how to implement Q-learning in Python. FREE ACCESS
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    9.  Exercise: Reinforcement Learning with Python
    4m 1s
    In this video, you will learn how to implement reinforcement learning using Python and TensorFlow, and how to implement Q-learning using Python. FREE ACCESS

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