Modern Reinforcement Learning: Actor Critic Methods from Paper to Code

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Modern Reinforcement Learning: Actor Critic Methods from Paper to Code

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Actor Critic Methods

72 Lessons

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Modern Reinforcement Learning: Actor Critic Methods from Paper to Code

Introduction

3 Lessons

What You Will Learn in This Course

Required Background, Software, and Hardware

How to Succeed in This Course

Fundamentals of Reinforcement Learning

6 Lessons

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Review of Fundamental Concepts

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Teaching an AI about Black Jack with Monte Carlo Prediction

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Teaching an AI How to Play Black Jack with Monte Carlo Control

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Review of Temporal Difference Learning Methods

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Teaching an AI about Balance with TD(0) Prediction

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Teaching an AI to Balance the Cart Pole with Q Learning

Landing on the Moon with Policy Gradients & Actor Critic Methods

11 Lessons

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What's so Great About Policy Gradient Methods?

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Combining Neural Networks with Monte Carlo: REINFORCE Policy Gradient Algorithm

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Introducing the Lunar Lander Environment

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Coding the Agent's Brain: The Policy Gradient Network

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Coding the Policy Gradient Agent's Basic Functionality

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Coding the Agent's Learn Function

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Coding the Policy Gradient Main Loop and Watching our Agent Land on the Moon

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Actor Critic Learning: Combining Policy Gradients & Temporal Difference Learning

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Coding the Actor Critic Networks

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Coding the Actor Critic Agent

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Coding the Actor Critic Main Loop and Watching Our Agent Land on the Moon

Deep Deterministic Policy Gradients (DDPG)

16 Lessons

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Getting up to Speed With Deep Q Learning

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How to Read and Understand Cutting Edge Research Papers

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Analyzing the DDPG Paper Abstract and Introduction

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Analyzing the Background Material

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What Algorithm Are We Going to Implement?

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What Results Should We Expect?

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What Other Solutions are Out There?

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What Model Architecture and Hyperparameters Do We Need?

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Handling the Explore-Exploit Dilemma: Coding the OU Action Noise Class

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Giving our Agent a Memory: Coding the Replay Memory Buffer Class

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Deep Q Learning for Actor Critic Methods: Coding the Critic Network Class

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Coding the Actor Network Class

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Giving our DDPG Agent Simple Autonomy: Coding the Basic Functions of Our Agent

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Giving our DDPG Agent a Brain: Coding the Agent's Learn Function

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Coding the Network Parameter Update Functionality

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Coding the Main Loop and Watching Our DDPG Agent Land on the Moon

Twin Delayed Deep Deterministic Policy Gradients (TD3)

12 Lessons

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Some Tips on Reading this Paper

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Analyzing the TD3 Paper Abstract and Introduction

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What Other Solutions Have People Tried?

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Reviewing the Fundamental Concepts

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Is Overestimation Bias Even a Problem in Actor-Critic Methods?

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Why is Variance a Problem for Actor-Critic Methods?

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What Results Can We Expect?

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Coding the Brains of the TD3 Agent - The Actor and Critic Network Classes

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Giving our TD3 Agent Simple Autonomy - Coding the Basic Agent Functionality

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Giving our TD3 Agent a Brain - Coding the Learn Function

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Coding the Network Parameter Update Functionality

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Coding the Main Loop And Watching our Agent Learn to Walk

Soft Actor Critic (SAC)

10 Lessons

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A Quick Word on the Paper

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Getting Acquainted With a New Framework for Learning

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Checking Out What Has Been Done Before

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Inspecting the Foundation of this New Framework

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Digging Into the Mathematics of Soft Actor Critic

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Seeing How the New Algorithm Measures Up

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Coding the Neural Networks

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Coding the Soft Actor Critic Basic Functionality

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Coding the Soft Actor Critic Algorithm

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Coding the Main Loop and Evaluating Our Agent

Tensorflow 2 Implementations

14 Lessons

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Coding the Policy Gradient Network in Tensorflow 2

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Coding the REINFORCE Agent in Tensorflow 2

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Coding the REINFORCE Main Loop and Evaluating Our Agent

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Coding the Actor Critic Network in Tensorflow 2

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Coding the Actor Critic Agent in Tensorflow 2

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Coding the Actor Critic Main Program and Evaluating our Agent

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Coding the DDPG Networks in Tensorflow 2

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Coding the DDPG Agent in Tensorflow 2

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Coding the DDPG Main Function and Evaluating our Agent

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Coding the TD3 Agent in Tensorflow 2

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Coding the TD3 Main Program and Evaluating Our Agent

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Coding the SAC Networks in Tensorflow 2

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Coding the SAC Agent in Tensorflow 2

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Coding the SAC Main Function and Evaluating our Agent

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About the teacher

phil

In 2012 I received my PhD in experimental condensed matter physics from West Virginia University. Following that I was a dry etch process engineer for Intel Corporation, where I leveraged big data to make essential process improvements for mission critical products. After leaving Intel in 2015, I have worked as a contract and freelance deep learning and artificial intelligence engineer.

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