Understanding Reinforcement Learning Environment and Rewards
Автор: MATLAB
Загружено: 2019-04-01
Просмотров: 46020
In this video, we build on our basic understanding of reinforcement learning by exploring the workflow. We cover what an environment is and some of the benefits of training within a simulated environment.
We cover what we ultimately want our agent to do and how crafting a reward function incentivizes the agent to do just that. And lastly, we introduce the need to choose a way to represent a policy—how we want to structure the parameters and logic that make up the decision-making part of the agent.
Watch our full video series about Reinforcement Learning: • Reinforcement Learning
By the end of this series, you’ll be better prepared to answer questions like:
What is reinforcement learning and why should I consider it when solving my control problem?
How do I set up and solve the reinforcement learning problem?
What are some of the benefits and drawbacks of reinforcement learning compared to a traditional controls approach?
Check out these other resources!
Reinforcement Learning by Sutton and Barto: http://bit.ly/2HAYbb4
Reinforcement Learning Course by David Silver: • RL Course by David Silver - Lecture 1: Int...
Reinforcement Learning Toolbox: https://bit.ly/2YjuAYa
Deep Reinforcement Learning for Walking Robots: • Deep Reinforcement Learning for Walking Ro...
Check out the individual videos in the series:
• What Is Reinforcement Learning?: • What Is Reinforcement Learning?
• Understanding the Environment and Rewards: • Understanding Reinforcement Learning Envir...
• Policies and Learning Algorithms: • Reinforcement Learning Policies and Learni...
• The Walking Robot Problem: • Solving the Walking Robot Problem with Rei...
• Overcoming the Practical Challenges: • Overcoming the Practical Challenges when u...
• An Introduction to Multi-Agent Reinforcement Learning: • Introduction to Multi-Agent Reinforcement ...
• Why Choose Model-Based Reinforcement Learning?: • Why Choose Model-Based Reinforcement Learn...
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