Advanced Reinforcement Learning: Implementation

Machine Learning
  • 11 Videos | 1h 38m 46s
  • Includes Assessment
  • Earns a Badge
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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

  • Playable
    1. 
    Course Overview
    1m 42s
    UP NEXT
  • Playable
    2. 
    Installing the Markov Decision Process Toolbox
    8m 8s
  • Locked
    3. 
    Rewards and Discounts
    11m 15s
  • Locked
    4. 
    Multi-Armed Bandit Problem
    10m 35s
  • Locked
    5. 
    Dynamic Programming and Bellman Equation
    6m 2s
  • Locked
    6. 
    Reinforcement Learning Agent and Its Components
    7m 13s
  • Locked
    7. 
    Reinforcement Learning with OpenAI Gym and Keras
    17m 3s
  • Locked
    8. 
    Reinforcement Learning Taxonomy by OpenAI
    8m 6s
  • Locked
    9. 
    Deep Q-Learning Implementation
    9m 48s
  • Locked
    10. 
    Training DNN Using Reinforcement Learning
    6m 48s
  • Locked
    11. 
    Exercise: Implementing Deep Q-Learning
    7m 38s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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