Modern Reinforcement Learning- Deep Q Agents (PyTorch TF2)

Modern Reinforcement Learning: Deep Q Agents (PyTorch & TF2), available at $79.99, has an average rating of 4.39, with 50 lectures, based on 1057 reviews, and has 6202 subscribers.

You will learn about How to read and implement deep reinforcement learning papers How to code Deep Q learning agents How to Code Double Deep Q Learning Agents How to Code Dueling Deep Q and Dueling Double Deep Q Learning Agents How to write modular and extensible deep reinforcement learning software How to automate hyperparameter tuning with command line arguments This course is ideal for individuals who are Python developers eager to learn about cutting edge deep reinforcement learning It is particularly useful for Python developers eager to learn about cutting edge deep reinforcement learning.

Enroll now: Modern Reinforcement Learning: Deep Q Agents (PyTorch & TF2)

Summary

Title: Modern Reinforcement Learning: Deep Q Agents (PyTorch & TF2)

Price: $79.99

Average Rating: 4.39

Number of Lectures: 50

Number of Published Lectures: 50

Number of Curriculum Items: 50

Number of Published Curriculum Objects: 50

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

How to read and implement deep reinforcement learning papers

How to code Deep Q learning agents

How to Code Double Deep Q Learning Agents

How to Code Dueling Deep Q and Dueling Double Deep Q Learning Agents

How to write modular and extensible deep reinforcement learning software

How to automate hyperparameter tuning with command line arguments

Who Should Attend

Python developers eager to learn about cutting edge deep reinforcement learning

Target Audiences

Python developers eager to learn about cutting edge deep reinforcement learning

In this complete deep reinforcement learning course you will learn a repeatable framework for reading and implementing deep reinforcement learning research papers. You will read the original papers that introduced the Deep Q learning, Double Deep Q learning, and Dueling Deep Q learning algorithms. You will then learn how to implement these in pythonic and concise PyTorch and Tensorflow 2code, that can be extended to include any future deep Q learning algorithms. These algorithms will be used to solve a variety of environments from the Open AI gym’s Atari library, including Pong, Breakout, and Bankheist.

You will learn the key to making these Deep Q Learning algorithms work, which is how to modify the Open AI Gym’s Atari library to meet the specifications of the original Deep Q Learning papers. You will learn how to:

Repeat actions to reduce computational overhead

Rescale the Atari screen images to increase efficiency

Stack frames to give the Deep Q agent a sense of motion

Evaluate the Deep Q agent’s performance with random no-ops to deal with model over training

Clip rewards to enable the Deep Q learning agent to generalize across Atari games with different score scales

If you do not have prior experience in reinforcement or deep reinforcement learning, that’s no problem. Included in the course is a complete and concise course on the fundamentals of reinforcement learning. The introductory course in reinforcement learning will be taught in the context of solving the Frozen Lake environment from the Open AI Gym.

We will cover:

Markov decision processes

Temporal difference learning

The original Q learning algorithm

How to solve the Bellman equation

Value functions and action value functions

Model free vs. model based reinforcement learning

Solutions to the explore-exploit dilemma, including optimistic initial values and epsilon-greedy action selection

Also included is a mini course in deep learning using the PyTorch framework. This is geared for students who are familiar with the basic concepts of deep learning, but not the specifics, or those who are comfortable with deep learning in another framework, such as Tensorflow or Keras. You will learn how to code a deep neural network in Pytorch as well as how convolutional neural networks function. This will be put to use in implementing a naive Deep Q learning agent to solve the Cartpole problem from the Open AI gym. 

Course Curriculum

Chapter 1: Introduction

Lecture 1: What You Will Learn In This Course

Lecture 2: Required Background, software, and hardware

Lecture 3: How to Succeed in this Course

Chapter 2: Fundamentals of Reinforcement Learning

Lecture 1: Agents, Environments, and Actions

Lecture 2: Markov Decision Processes

Lecture 3: Value Functions, Action Value Functions, and the Bellman Equation

Lecture 4: Model Free vs. Model Based Learning

Lecture 5: The Explore-Exploit Dilemma

Lecture 6: Temporal Difference Learning

Chapter 3: Deep Learning Crash Course

Lecture 1: Dealing with Continuous State Spaces with Deep Neural Networks

Lecture 2: Naive Deep Q Learning in Code: Step 1 – Coding the Deep Q Network

Lecture 3: Naive Deep Q Learning in Code: Step 2 – Coding the Agent Class

Lecture 4: Naive Deep Q Learning in Code: Step 3 – Coding the Main Loop and Learning

Lecture 5: Naive Deep Q Learning in Code: Step 4 – Verifying the Functionality of Our Code

Lecture 6: Naive Deep Q Learning in Code: Step 5 – Analyzing Our Agents Performance

Lecture 7: Dealing with Screen Images with Convolutional Neural Networks

Chapter 4: Human Level Control Through Deep Reinforcement Learning: From Paper to Code

Lecture 1: How to Read Deep Learning Papers

Lecture 2: Analyzing the Paper

Lecture 3: How to Modify the OpenAI Gym Atari Environments

Lecture 4: How to Preprocess the OpenAI Gym Atari Screen Images

Lecture 5: How to Stack the Preprocessed Atari Screen Images

Lecture 6: How to Combine All the Changes

Lecture 7: How to Add Reward Clipping, Fire First, and No Ops

Lecture 8: How to Code the Agents Memory

Lecture 9: How to Code the Deep Q Network

Lecture 10: Coding the Deep Q Agent: Step 1 – Coding the Constructor

Lecture 11: Coding the Deep Q Agent: Step 2 – Epsilon-Greedy Action Selection

Lecture 12: Coding the Deep Q Agent: Step 3 – Memory, Model Saving and Network Copying

Lecture 13: Coding the Deep Q Agent: Step 4 – The Agents Learn Function

Lecture 14: Coding the Deep Q Agent: Step 5 – The Main Loop and Analyzing the Performance

Chapter 5: Deep Reinforcement Learning with Double Q Learning

Lecture 1: Analyzing the Paper

Lecture 2: Coding the Double Q Learning Agent and Analyzing Performance

Chapter 6: Dueling Network Architectures for Deep Reinforcement Learning

Lecture 1: Analyzing the Paper

Lecture 2: Coding the Dueling Deep Q Network

Lecture 3: Coding the Dueling Deep Q Learning Agent and Analyzing Performance

Lecture 4: Coding the Dueling Double Deep Q Learning Agent and Analyzing Performance

Chapter 7: Improving On Our Solutions

Lecture 1: Implementing a Command Line Interface for Rapid Model Testing

Lecture 2: Consolidating Our Code Base for Maximum Extensability

Lecture 3: How to Test Our Agent and Watch it Play the Game in Real Time

Chapter 8: Conclusion

Lecture 1: Summarizing What Weve Learned

Chapter 9: Bonus Lecture

Lecture 1: Bonus Video: Where to Go From Here

Chapter 10: Tensorflow 2 Implementations

Lecture 1: Differences Between Tensorflow 2 and PyTorch

Lecture 2: Coding the Deep Q Network Class in Tensorflow 2

Lecture 3: Coding the Deep Q Learning Agent in Tensorflow 2

Lecture 4: Testing the Tensorflow 2 Deep Q Learning Agent

Lecture 5: Coding the Tensorflow 2 Double Q Learning Agent

Lecture 6: Coding the Dueling Network and Agent in Tensorflow 2

Lecture 7: Coding the Dueling Double DQN Agent in Tensorflow 2

Chapter 11: Appendix

Lecture 1: Installing the New OpenAI Gym in a Virtual Environment

Lecture 2: Making the DQN Agent Compliant with the New Gym Interface

Instructors

Phil Tabor
Machine Learning Engineer

Rating Distribution

1 stars: 8 votes

2 stars: 17 votes

3 stars: 72 votes

4 stars: 318 votes

5 stars: 642 votes

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