Advanced Reinforcement Learning: Implementation

Machine Learning    |    Intermediate
  • 11 videos | 1h 34m 16s
  • Includes Assessment
  • Earns a Badge
Rating 4.5 of 6 users Rating 4.5 of 6 users (6)
In this 11-video course, learners can examine the role of reward and discount factors in reinforcement learning, as well as the multi-armed bandit problem and approaches to solving it for machine learning. You will begin by learning how to install the Markov Decision Policy (MDP) toolbox and implement the Discounted Markov Decision Process using the policy iteration algorithm. Next, examine the role of reward and discount factors in reinforcement learning, and the multi-armed bandit problem and solutions. Learn about dynamic programming, policy evaluation, policy iteration, value iteration, and characteristics of Bellman equation. Then learners will explore reinforcement learning agent components and applications; work with reinforcement learning agents using Keras and OpenAI Gym; describe reinforcement learning algorithms and the reinforcement learning taxonomy defined by OpenAI; and implement deep Q-learning with Keras. Finally, observe how to train deep neural networks (DNN) with reinforcement learning for time series forecasting. In the closing exercise, you will recall approaches for resolving the multi-armed bandit problem, list reinforcement learning agent components, and implement deep Q-learning by using Keras and OpenAI Gym.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Install the markov decision policy toolbox and implement the discounted markov decision process using the policy iteration algorithm
    Recognize the role of reward and discount factors in reinforcement learning
    Describe the multi-armed bandit problem and different approaches of solving this problem
    Describe dynamic programming, policy evaluation, policy iteration, value iteration, and characteristics of bellman equation
    List reinforcement learning agent components and reinforcement agent applications
  • Work with reinforcement learning agents using keras and openai gym
    Describe reinforcement learning algorithms and the reinforcement learning taxonomy defined by openai
    Implement deep reinforcement learning or deep q-learning using keras and openai gym
    Recognize how to train deep neural networks using reinforcement learning for time series forecasting
    Recall approaches for resolving the multi-armed bandit problem, list reinforcement learning agent components, and implement deep q-learning using keras and openai gym

IN THIS COURSE

  • 1m 42s
  • 8m 8s
    In this video, you will learn how to install the Markov Decision Policy toolbox and implement the Discounted Markov Decision Process using the policy iteration algorithm. FREE ACCESS
  • Locked
    3.  Rewards and Discounts
    11m 15s
    Upon completion of this video, you will be able to recognize the role of reward and discount factors in reinforcement learning. FREE ACCESS
  • Locked
    4.  Multi-Armed Bandit Problem
    10m 35s
    After completing this video, you will be able to describe the multi-armed bandit problem and different approaches for solving this problem. FREE ACCESS
  • Locked
    5.  Dynamic Programming and Bellman Equation
    6m 2s
    After completing this video, you will be able to describe dynamic programming, policy evaluation, policy iteration, value iteration, and characteristics of the Bellman equation. FREE ACCESS
  • Locked
    6.  Reinforcement Learning Agent and Its Components
    7m 13s
    After completing this video, you will be able to list the components of a reinforcement learning agent and its applications. FREE ACCESS
  • Locked
    7.  Reinforcement Learning with OpenAI Gym and Keras
    17m 3s
    Find out how to work with reinforcement learning agents using Keras and OpenAI Gym. FREE ACCESS
  • Locked
    8.  Reinforcement Learning Taxonomy by OpenAI
    8m 6s
    Upon completion of this video, you will be able to describe reinforcement learning algorithms and the reinforcement learning taxonomy defined by OpenAI. FREE ACCESS
  • Locked
    9.  Deep Q-Learning Implementation
    9m 48s
    In this video, you will learn how to implement deep reinforcement learning or deep Q-learning using Keras and OpenAI Gym. FREE ACCESS
  • Locked
    10.  Training DNN Using Reinforcement Learning
    6m 48s
    After completing this video, you will be able to recognize how to train deep neural networks using reinforcement learning for time series forecasting. FREE ACCESS
  • Locked
    11.  Exercise: Implementing Deep Q-Learning
    7m 38s
    Upon completion of this video, you will be able to recall approaches for resolving the multi-armed bandit problem, list reinforcement learning agent components, and implement deep Q-learning using Keras and OpenAI Gym. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.

Digital badges are yours to keep, forever.

YOU MIGHT ALSO LIKE

Book Distributed Machine Learning Patterns
Book Advanced Machine Learning: Fundamentals and Algorithms
Book Representation in Machine Learning

PEOPLE WHO VIEWED THIS ALSO VIEWED THESE

Course Advanced Reinforcement Learning: Principles
Rating 4.3 of 8 users Rating 4.3 of 8 users (8)
Course Generative AI APIs for Practical Applications: An Introduction
Rating 4.6 of 18 users Rating 4.6 of 18 users (18)
Course Overview of VMWare
Rating 4.7 of 205 users Rating 4.7 of 205 users (205)