- What You Will Learn in this Course
- Required Background, Software, and Hardware
- How to Succeed in this Course
Modern Reinforcement Learning: Actor-Critic Algorithms
Quick Facts
particular | details | |||
---|---|---|---|---|
Medium of instructions
English
|
Mode of learning
Self study
|
Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
certificate availability
Yes
certificate providing authority
Udemy
The syllabus
Introduction
Fundamentals of Reinforcement Learning
- Review of Fundamental Concepts
- Calculating State Transition Probabilities
- Teaching an AI about Black Jack with Monte Carlo Prediction
- Teaching an AI How to Play Black Jack with Monte Carlo Control
- Review of Temporal Difference Learning Methods
- Teaching an AI about Balance with TD(0) Prediction
- Teaching an AI to Balance the Cart Pole with Q Learning
Landing on the Moon with Policy Gradients & Actor Critic Methods
- What's so Great About Policy Gradient Methods?
- Combining Neural Networks with Monte Carlo: REINFORCE Policy Gradient Algorithm
- Introducing the Lunar Lander Environment
- Coding the Agent's Brain: The Policy Gradient Network
- Coding the Policy Gradient Agent's Basic Functionality
- Coding the Agent's Learn Function
- Coding the Policy Gradient Main Loop and Watching our Agent Land on the Moon
- Actor Critic Learning: Combining Policy Gradients & Temporal Difference Learning
- Coding the Actor Critic Networks
- Coding the Actor Critic Agent
- Coding the Actor Critic Main Loop and Watching Our Agent Land on the Moon
Deep Deterministic Policy Gradients (DDPG): Actor Critic with Continuous Actions
- Getting up to Speed With Deep Q Learning
- How to Read and Understand Cutting Edge Research Papers
- Analyzing the DDPG Paper Abstract and Introduction
- Analyzing the Background Material
- What Algorithm Are We Going to Implement?
- What Results Should We Expect?
- What Other Solutions are Out There?
- What Model Architecture and Hyperparameters Do We Need?
- Handling the Explore-Exploit Dilemma: Coding the OU Action Noise Class
- Giving our Agent a Memory: Coding the Replay Memory Buffer Class
- Deep Q Learning for Actor Critic Methods: Coding the Critic Network Class
- Coding the Actor Network Class
- Giving our DDPG Agent Simple Autonomy: Coding the Basic Functions of Our Agent
- Giving our DDPG Agent a Brain: Coding the Agent's Learn Function
- Coding the Network Parameter Update Functionality
- Coding the Main Loop and Watching Our DDPG Agent Land on the Moon
Twin Delayed Deep Deterministic Policy Gradients (TD3)
- Some Tips on Reading this Paper
- Analyzing the TD3 Paper Abstract and Introduction
- What Other Solutions Have People Tried?
- Reviewing the Fundamental Concepts
- Is Overestimation Bias Even a Problem in Actor-Critic Methods?
- Why is Variance a Problem for Actor-Critic Methods?
- What Results Can We Expect?
- Coding the Brains of the TD3 Agent - The Actor and Critic Network Classes
- Giving our TD3 Agent Simple Autonomy - Coding the Basic Agent Functionality
- Giving our TD3 Agent a Brain - Coding the Learn Function
- Coding the Network Parameter Update Functionality
- Coding the Main Loop And Watching our Agent Learn to Walk
Soft Actor Critic
- A Quick Word on the Paper
- Getting Acquainted With a New Framework
- Checking Out What Has Been Done Before
- Inspecting the Foundation of this New Framework
- Digging Into the Mathematics of Soft Actor Critic
- Seeing How the New Algorithm Measures Up
- Coding the Neural Networks
- Coding the Soft Actor Critic Basic Functionality
- Coding the Soft Actor Critic Algorithm
- Coding the Main Loop and Evaluating Our Agent
Articles
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