-
Reinforcement Learning: Crash Course AI #9
Reinforcement learning is particularly useful in situations where we want to train AIs to have certain skills we don’t fully understand ourselves. Unlike some of the techniques we’ve discussed so far, reinforcement learning generally only looks at how an AI performs a task AFTER it has completed it. And when an AI completes that task figuring out when and how to reward an AI, called credit assignment, is one of the hardest parts of reinforcement learning. So today, we’re going to explore these ideas, introduce a ton of new terms like value, policy, agent, environment, actions, and states and we’ll show you how we can use strategies like exploration and exploitation to train John Green Bot to find things more efficiently next time.
Crash Course AI is produced in association with PBS Digi...
published: 11 Oct 2019
-
Introduction to Reinforcement Learning | Scope of Reinforcement Learning by Mahesh Huddar
Introduction to Reinforcement Learning | Scope of Reinforcement Learning by Mahesh Huddar
Introduction to Reinforcement Learning: https://www.youtube.com/watch?v=PIfj8jJuO1s
Q Learning Algorithm Explained: https://www.youtube.com/watch?v=_O9a5xbBe-s
#1. Q Learning Algorithm Solved Example: https://www.youtube.com/watch?v=J3qX50yyiU0
Characteristics, Challenges, and Applications of Reinforcement Learning: https://www.youtube.com/watch?v=rMA_0I_e5dQ
The following concepts are discussed:
______________________________
Introduction to Reinforcement Learning,
Scope of Reinforcement Learning,
reinforcement learning in machine learning,
reinforcement learning tutorial,
reinforcement learning in artificial intelligence,
reinforcement learning algorithms
********************************
1. Bl...
published: 23 Nov 2022
-
DESTROYING Donkey Kong with AI (Deep Reinforcement Learning)
Go to https://brilliant.org/CodeBullet/ to get a 30-day free trial + the first 200 people will get 20% off their annual subscription.
Second Channel: https://www.youtube.com/@codebulletsdayoff582
Twitter: https://twitter.com/code_bullet
Patreon: https://www.patreon.com/CodeBullet
Discord: https://discord.gg/UZDMYx5
Art created by @Dachi.art https://www.instagram.com/dachi.art
published: 25 Feb 2023
-
Reinforcement Learning Basics
In this video, you'll get a comprehensive introduction to reinforcement learning. Whether you're a beginner or looking to refine your skills, this video will help you master the fundamentals. 🌟
If you enjoyed this video and want to dive deeper into the world of programming, machine learning, AI, reinforcement learning, deep learning, and more then check out our full catalog: https://bit.ly/3Vr0uRo. Forge your future in tech with Udacity!
---
Connect with us on social! 🌐
Instagram: https://www.instagram.com/udacity/
LinkedIn: https://www.linkedin.com/school/udacity/
Facebook: https://www.facebook.com/Udacity/
X/Twitter: https://twitter.com/udacity
published: 06 Jun 2016
-
AI Learns to Walk (deep reinforcement learning)
AI Teaches Itself to Walk!
In this video an AI Warehouse agent named Albert learns how to walk to escape 5 rooms I created. The AI was trained using Deep Reinforcement Learning, a method of Machine Learning which involves rewarding the agent for doing something correctly, and punishing it for doing anything incorrectly. Albert's actions are controlled by a Neural Network that's updated after each attempt in order to try to give Albert more rewards and less punishments over time. Check the pinned comment for more information on how the AI was trained!
Current Subscribers: 135,027
published: 23 Apr 2023
-
An introduction to Reinforcement Learning
This episode gives a general introduction into the field of Reinforcement Learning:
- High level description of the field
- Policy gradients
- Biggest challenges (sparse rewards, reward shaping, ...)
This video forms the basis for a series on RL where I will dive much deeper into technical details of state-of-the-art methods for RL.
Links:
- "Pong from Pixels - Karpathy": http://karpathy.github.io/2016/05/31/rl/
- Concept networks for grasp & stack (Paper with heavy reward shaping): https://arxiv.org/abs/1709.06977
If you enjoy my videos, all support is super welcome!
https://www.patreon.com/ArxivInsights
If you have questions you would like to discuss with me personally, you can book a 1-on-1 video call through Pensight: https://pensight.com/x/xander-steenbrugge
::Chapters::
00:00 In...
published: 02 Apr 2018
-
MIT 6.S191: Reinforcement Learning
MIT Introduction to Deep Learning 6.S191: Lecture 5
Deep Reinforcement Learning
Lecturer: Alexander Amini
2023 Edition
For all lectures, slides, and lab materials: http://introtodeeplearning.com
Lecture Outline:
0:00 - Introduction
3:49 - Classes of learning problems
6:48 - Definitions
12:24 - The Q function
17:06 - Deeper into the Q function
21:32 - Deep Q Networks
29:15 - Atari results and limitations
32:42 - Policy learning algorithms
36:42 - Discrete vs continuous actions
39:48 - Training policy gradients
47:17 - RL in real life
49:55 - VISTA simulator
52:04 - AlphaGo and AlphaZero and MuZero
56:34 - Summary
Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!
published: 14 Apr 2023
-
AI Learns to Escape (deep reinforcement learning)
AI Teaches Itself How to Escape!
In this video an AI Warehouse agent named Albert learns how to escape 5 rooms I've designed. The AI was trained using Deep Reinforcement Learning, a method of Machine Learning which involves rewarding the agent for doing something correctly, and punishing it for doing anything incorrectly. Albert's actions are controlled by a Neural Network that's updated after each attempt in order to try to give Albert more rewards and less punishments over time.
Everything in this video (except for the music) was created entirely by myself using Unity. Check the pinned comment for more information on how the AI was trained!
Current Subscribers: 0
published: 29 Oct 2022
-
Q function and Value Function Concepts | Reinforcement Learning Algorithms
Welcome to our insightful discussion on two critical concepts in the world of reinforcement learning: the Value Function and the Q-Function. These functions play a key role in estimating the expected cumulative reward in different scenarios, but they do so through distinct approaches.
Throughout this discussion, we'll delve into the nuances of these functions and explore their key differences, providing you with a deeper understanding of their significance in the realm of reinforcement learning. Let's embark on this journey of discovery together!
Value function
1) The value function estimates the expected cumulative reward of being in a particular state.
2) It is a state function, meaning that it only takes the state as input.
3) The value function can be used to evaluate different polic...
published: 23 Jul 2023
-
Reinforcement Learning: Machine Learning Meets Control Theory
Reinforcement learning is a powerful technique at the intersection of machine learning and control theory, and it is inspired by how biological systems learn to interact with their environment. In this video, we provide a high level overview of reinforcement learning, along with leading algorithms and impressive applications.
Citable link for this video: https://doi.org/10.52843/cassyni.x2t0sp
@eigensteve on Twitter
eigensteve.com
databookuw.com
This video was produced at the University of Washington
%%% CHAPTERS %%%
0:00 Introduction
3:34 Reinforcement Learning Overview
7:30 Mathematics of Reinforcement Learning
12:32 Markov Decision Process
13:33 Credit Assignment Problem
15:38 Optimization Techniques for RL
18:54 Examples of Reinforcement Learning
21:50 Q-Learning
23:53 Hindsight...
published: 12 Feb 2021
11:28
Reinforcement Learning: Crash Course AI #9
Reinforcement learning is particularly useful in situations where we want to train AIs to have certain skills we don’t fully understand ourselves. Unlike some o...
Reinforcement learning is particularly useful in situations where we want to train AIs to have certain skills we don’t fully understand ourselves. Unlike some of the techniques we’ve discussed so far, reinforcement learning generally only looks at how an AI performs a task AFTER it has completed it. And when an AI completes that task figuring out when and how to reward an AI, called credit assignment, is one of the hardest parts of reinforcement learning. So today, we’re going to explore these ideas, introduce a ton of new terms like value, policy, agent, environment, actions, and states and we’ll show you how we can use strategies like exploration and exploitation to train John Green Bot to find things more efficiently next time.
Crash Course AI is produced in association with PBS Digital Studios:
https://www.youtube.com/user/pbsdigitalstudios/videos
Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse
Thanks to the following patrons for their generous monthly contributions that help keep Crash Course free for everyone forever:
Eric Prestemon, Sam Buck, Mark Brouwer, Indika Siriwardena, Avi Yashchin, Timothy J Kwist, Brian Thomas Gossett, Haixiang N/A Liu, Jonathan Zbikowski, Siobhan Sabino, Zach Van Stanley, Jennifer Killen, Nathan Catchings, Brandon Westmoreland, dorsey, Kenneth F Penttinen, Trevin Beattie, Erika & Alexa Saur, Justin Zingsheim, Jessica Wode, Tom Trval, Jason Saslow, Nathan Taylor, Khaled El Shalakany, SR Foxley, Sam Ferguson, Yasenia Cruz, Eric Koslow, Caleb Weeks, Tim Curwick, David Noe, Shawn Arnold, William McGraw, Andrei Krishkevich, Rachel Bright, Jirat, Ian Dundore
--
Want to find Crash Course elsewhere on the internet?
Facebook - http://www.facebook.com/YouTubeCrashCourse
Twitter - http://www.twitter.com/TheCrashCourse
Tumblr - http://thecrashcourse.tumblr.com
Support Crash Course on Patreon: http://patreon.com/crashcourse
CC Kids: http://www.youtube.com/crashcoursekids
#CrashCourse #ArtificialIntelligence #MachineLearning
https://wn.com/Reinforcement_Learning_Crash_Course_Ai_9
Reinforcement learning is particularly useful in situations where we want to train AIs to have certain skills we don’t fully understand ourselves. Unlike some of the techniques we’ve discussed so far, reinforcement learning generally only looks at how an AI performs a task AFTER it has completed it. And when an AI completes that task figuring out when and how to reward an AI, called credit assignment, is one of the hardest parts of reinforcement learning. So today, we’re going to explore these ideas, introduce a ton of new terms like value, policy, agent, environment, actions, and states and we’ll show you how we can use strategies like exploration and exploitation to train John Green Bot to find things more efficiently next time.
Crash Course AI is produced in association with PBS Digital Studios:
https://www.youtube.com/user/pbsdigitalstudios/videos
Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse
Thanks to the following patrons for their generous monthly contributions that help keep Crash Course free for everyone forever:
Eric Prestemon, Sam Buck, Mark Brouwer, Indika Siriwardena, Avi Yashchin, Timothy J Kwist, Brian Thomas Gossett, Haixiang N/A Liu, Jonathan Zbikowski, Siobhan Sabino, Zach Van Stanley, Jennifer Killen, Nathan Catchings, Brandon Westmoreland, dorsey, Kenneth F Penttinen, Trevin Beattie, Erika & Alexa Saur, Justin Zingsheim, Jessica Wode, Tom Trval, Jason Saslow, Nathan Taylor, Khaled El Shalakany, SR Foxley, Sam Ferguson, Yasenia Cruz, Eric Koslow, Caleb Weeks, Tim Curwick, David Noe, Shawn Arnold, William McGraw, Andrei Krishkevich, Rachel Bright, Jirat, Ian Dundore
--
Want to find Crash Course elsewhere on the internet?
Facebook - http://www.facebook.com/YouTubeCrashCourse
Twitter - http://www.twitter.com/TheCrashCourse
Tumblr - http://thecrashcourse.tumblr.com
Support Crash Course on Patreon: http://patreon.com/crashcourse
CC Kids: http://www.youtube.com/crashcoursekids
#CrashCourse #ArtificialIntelligence #MachineLearning
- published: 11 Oct 2019
- views: 142888
8:56
Introduction to Reinforcement Learning | Scope of Reinforcement Learning by Mahesh Huddar
Introduction to Reinforcement Learning | Scope of Reinforcement Learning by Mahesh Huddar
Introduction to Reinforcement Learning: https://www.youtube.com/watch...
Introduction to Reinforcement Learning | Scope of Reinforcement Learning by Mahesh Huddar
Introduction to Reinforcement Learning: https://www.youtube.com/watch?v=PIfj8jJuO1s
Q Learning Algorithm Explained: https://www.youtube.com/watch?v=_O9a5xbBe-s
#1. Q Learning Algorithm Solved Example: https://www.youtube.com/watch?v=J3qX50yyiU0
Characteristics, Challenges, and Applications of Reinforcement Learning: https://www.youtube.com/watch?v=rMA_0I_e5dQ
The following concepts are discussed:
______________________________
Introduction to Reinforcement Learning,
Scope of Reinforcement Learning,
reinforcement learning in machine learning,
reinforcement learning tutorial,
reinforcement learning in artificial intelligence,
reinforcement learning algorithms
********************************
1. Blog / Website: https://www.vtupulse.com/
2. Like Facebook Page: https://www.facebook.com/VTUPulse
3. Follow us on Instagram: https://www.instagram.com/vtupulse/
4. Like, Share, Subscribe, and Don't forget to press the bell ICON for regular updates
https://wn.com/Introduction_To_Reinforcement_Learning_|_Scope_Of_Reinforcement_Learning_By_Mahesh_Huddar
Introduction to Reinforcement Learning | Scope of Reinforcement Learning by Mahesh Huddar
Introduction to Reinforcement Learning: https://www.youtube.com/watch?v=PIfj8jJuO1s
Q Learning Algorithm Explained: https://www.youtube.com/watch?v=_O9a5xbBe-s
#1. Q Learning Algorithm Solved Example: https://www.youtube.com/watch?v=J3qX50yyiU0
Characteristics, Challenges, and Applications of Reinforcement Learning: https://www.youtube.com/watch?v=rMA_0I_e5dQ
The following concepts are discussed:
______________________________
Introduction to Reinforcement Learning,
Scope of Reinforcement Learning,
reinforcement learning in machine learning,
reinforcement learning tutorial,
reinforcement learning in artificial intelligence,
reinforcement learning algorithms
********************************
1. Blog / Website: https://www.vtupulse.com/
2. Like Facebook Page: https://www.facebook.com/VTUPulse
3. Follow us on Instagram: https://www.instagram.com/vtupulse/
4. Like, Share, Subscribe, and Don't forget to press the bell ICON for regular updates
- published: 23 Nov 2022
- views: 118537
29:46
DESTROYING Donkey Kong with AI (Deep Reinforcement Learning)
Go to https://brilliant.org/CodeBullet/ to get a 30-day free trial + the first 200 people will get 20% off their annual subscription.
Second Channel: https://w...
Go to https://brilliant.org/CodeBullet/ to get a 30-day free trial + the first 200 people will get 20% off their annual subscription.
Second Channel: https://www.youtube.com/@codebulletsdayoff582
Twitter: https://twitter.com/code_bullet
Patreon: https://www.patreon.com/CodeBullet
Discord: https://discord.gg/UZDMYx5
Art created by @Dachi.art https://www.instagram.com/dachi.art
https://wn.com/Destroying_Donkey_Kong_With_Ai_(Deep_Reinforcement_Learning)
Go to https://brilliant.org/CodeBullet/ to get a 30-day free trial + the first 200 people will get 20% off their annual subscription.
Second Channel: https://www.youtube.com/@codebulletsdayoff582
Twitter: https://twitter.com/code_bullet
Patreon: https://www.patreon.com/CodeBullet
Discord: https://discord.gg/UZDMYx5
Art created by @Dachi.art https://www.instagram.com/dachi.art
- published: 25 Feb 2023
- views: 2744514
2:28
Reinforcement Learning Basics
In this video, you'll get a comprehensive introduction to reinforcement learning. Whether you're a beginner or looking to refine your skills, this video will he...
In this video, you'll get a comprehensive introduction to reinforcement learning. Whether you're a beginner or looking to refine your skills, this video will help you master the fundamentals. 🌟
If you enjoyed this video and want to dive deeper into the world of programming, machine learning, AI, reinforcement learning, deep learning, and more then check out our full catalog: https://bit.ly/3Vr0uRo. Forge your future in tech with Udacity!
---
Connect with us on social! 🌐
Instagram: https://www.instagram.com/udacity/
LinkedIn: https://www.linkedin.com/school/udacity/
Facebook: https://www.facebook.com/Udacity/
X/Twitter: https://twitter.com/udacity
https://wn.com/Reinforcement_Learning_Basics
In this video, you'll get a comprehensive introduction to reinforcement learning. Whether you're a beginner or looking to refine your skills, this video will help you master the fundamentals. 🌟
If you enjoyed this video and want to dive deeper into the world of programming, machine learning, AI, reinforcement learning, deep learning, and more then check out our full catalog: https://bit.ly/3Vr0uRo. Forge your future in tech with Udacity!
---
Connect with us on social! 🌐
Instagram: https://www.instagram.com/udacity/
LinkedIn: https://www.linkedin.com/school/udacity/
Facebook: https://www.facebook.com/Udacity/
X/Twitter: https://twitter.com/udacity
- published: 06 Jun 2016
- views: 232615
8:40
AI Learns to Walk (deep reinforcement learning)
AI Teaches Itself to Walk!
In this video an AI Warehouse agent named Albert learns how to walk to escape 5 rooms I created. The AI was trained using Deep Reinf...
AI Teaches Itself to Walk!
In this video an AI Warehouse agent named Albert learns how to walk to escape 5 rooms I created. The AI was trained using Deep Reinforcement Learning, a method of Machine Learning which involves rewarding the agent for doing something correctly, and punishing it for doing anything incorrectly. Albert's actions are controlled by a Neural Network that's updated after each attempt in order to try to give Albert more rewards and less punishments over time. Check the pinned comment for more information on how the AI was trained!
Current Subscribers: 135,027
https://wn.com/Ai_Learns_To_Walk_(Deep_Reinforcement_Learning)
AI Teaches Itself to Walk!
In this video an AI Warehouse agent named Albert learns how to walk to escape 5 rooms I created. The AI was trained using Deep Reinforcement Learning, a method of Machine Learning which involves rewarding the agent for doing something correctly, and punishing it for doing anything incorrectly. Albert's actions are controlled by a Neural Network that's updated after each attempt in order to try to give Albert more rewards and less punishments over time. Check the pinned comment for more information on how the AI was trained!
Current Subscribers: 135,027
- published: 23 Apr 2023
- views: 9141969
16:27
An introduction to Reinforcement Learning
This episode gives a general introduction into the field of Reinforcement Learning:
- High level description of the field
- Policy gradients
- Biggest challenge...
This episode gives a general introduction into the field of Reinforcement Learning:
- High level description of the field
- Policy gradients
- Biggest challenges (sparse rewards, reward shaping, ...)
This video forms the basis for a series on RL where I will dive much deeper into technical details of state-of-the-art methods for RL.
Links:
- "Pong from Pixels - Karpathy": http://karpathy.github.io/2016/05/31/rl/
- Concept networks for grasp & stack (Paper with heavy reward shaping): https://arxiv.org/abs/1709.06977
If you enjoy my videos, all support is super welcome!
https://www.patreon.com/ArxivInsights
If you have questions you would like to discuss with me personally, you can book a 1-on-1 video call through Pensight: https://pensight.com/x/xander-steenbrugge
::Chapters::
00:00 Intro
01:03 So what is Reinforcement Learning?
03:39 Learning without explicit examples
07:25 Main challenges when doing RL
15:04 Are the robots taking over now?
https://wn.com/An_Introduction_To_Reinforcement_Learning
This episode gives a general introduction into the field of Reinforcement Learning:
- High level description of the field
- Policy gradients
- Biggest challenges (sparse rewards, reward shaping, ...)
This video forms the basis for a series on RL where I will dive much deeper into technical details of state-of-the-art methods for RL.
Links:
- "Pong from Pixels - Karpathy": http://karpathy.github.io/2016/05/31/rl/
- Concept networks for grasp & stack (Paper with heavy reward shaping): https://arxiv.org/abs/1709.06977
If you enjoy my videos, all support is super welcome!
https://www.patreon.com/ArxivInsights
If you have questions you would like to discuss with me personally, you can book a 1-on-1 video call through Pensight: https://pensight.com/x/xander-steenbrugge
::Chapters::
00:00 Intro
01:03 So what is Reinforcement Learning?
03:39 Learning without explicit examples
07:25 Main challenges when doing RL
15:04 Are the robots taking over now?
- published: 02 Apr 2018
- views: 627745
57:33
MIT 6.S191: Reinforcement Learning
MIT Introduction to Deep Learning 6.S191: Lecture 5
Deep Reinforcement Learning
Lecturer: Alexander Amini
2023 Edition
For all lectures, slides, and lab materi...
MIT Introduction to Deep Learning 6.S191: Lecture 5
Deep Reinforcement Learning
Lecturer: Alexander Amini
2023 Edition
For all lectures, slides, and lab materials: http://introtodeeplearning.com
Lecture Outline:
0:00 - Introduction
3:49 - Classes of learning problems
6:48 - Definitions
12:24 - The Q function
17:06 - Deeper into the Q function
21:32 - Deep Q Networks
29:15 - Atari results and limitations
32:42 - Policy learning algorithms
36:42 - Discrete vs continuous actions
39:48 - Training policy gradients
47:17 - RL in real life
49:55 - VISTA simulator
52:04 - AlphaGo and AlphaZero and MuZero
56:34 - Summary
Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!
https://wn.com/Mit_6.S191_Reinforcement_Learning
MIT Introduction to Deep Learning 6.S191: Lecture 5
Deep Reinforcement Learning
Lecturer: Alexander Amini
2023 Edition
For all lectures, slides, and lab materials: http://introtodeeplearning.com
Lecture Outline:
0:00 - Introduction
3:49 - Classes of learning problems
6:48 - Definitions
12:24 - The Q function
17:06 - Deeper into the Q function
21:32 - Deep Q Networks
29:15 - Atari results and limitations
32:42 - Policy learning algorithms
36:42 - Discrete vs continuous actions
39:48 - Training policy gradients
47:17 - RL in real life
49:55 - VISTA simulator
52:04 - AlphaGo and AlphaZero and MuZero
56:34 - Summary
Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!
- published: 14 Apr 2023
- views: 39048
8:18
AI Learns to Escape (deep reinforcement learning)
AI Teaches Itself How to Escape!
In this video an AI Warehouse agent named Albert learns how to escape 5 rooms I've designed. The AI was trained using Deep Rei...
AI Teaches Itself How to Escape!
In this video an AI Warehouse agent named Albert learns how to escape 5 rooms I've designed. The AI was trained using Deep Reinforcement Learning, a method of Machine Learning which involves rewarding the agent for doing something correctly, and punishing it for doing anything incorrectly. Albert's actions are controlled by a Neural Network that's updated after each attempt in order to try to give Albert more rewards and less punishments over time.
Everything in this video (except for the music) was created entirely by myself using Unity. Check the pinned comment for more information on how the AI was trained!
Current Subscribers: 0
https://wn.com/Ai_Learns_To_Escape_(Deep_Reinforcement_Learning)
AI Teaches Itself How to Escape!
In this video an AI Warehouse agent named Albert learns how to escape 5 rooms I've designed. The AI was trained using Deep Reinforcement Learning, a method of Machine Learning which involves rewarding the agent for doing something correctly, and punishing it for doing anything incorrectly. Albert's actions are controlled by a Neural Network that's updated after each attempt in order to try to give Albert more rewards and less punishments over time.
Everything in this video (except for the music) was created entirely by myself using Unity. Check the pinned comment for more information on how the AI was trained!
Current Subscribers: 0
- published: 29 Oct 2022
- views: 6206868
5:55
Q function and Value Function Concepts | Reinforcement Learning Algorithms
Welcome to our insightful discussion on two critical concepts in the world of reinforcement learning: the Value Function and the Q-Function. These functions pla...
Welcome to our insightful discussion on two critical concepts in the world of reinforcement learning: the Value Function and the Q-Function. These functions play a key role in estimating the expected cumulative reward in different scenarios, but they do so through distinct approaches.
Throughout this discussion, we'll delve into the nuances of these functions and explore their key differences, providing you with a deeper understanding of their significance in the realm of reinforcement learning. Let's embark on this journey of discovery together!
Value function
1) The value function estimates the expected cumulative reward of being in a particular state.
2) It is a state function, meaning that it only takes the state as input.
3) The value function can be used to evaluate different policies, and to find the optimal policy.
Q-function
1) The Q function estimates the expected cumulative reward of taking a particular action in a given state.
2) It is a state-action function, meaning that it takes both the state and the action as input.
3) The Q function is used to learn an optimal policy, which is a policy that maximizes the expected cumulative reward.
Key Difference between Q-Function and Value Function
The Q function and the value function are both used to estimate the expected cumulative reward, but they do so in different ways. The Q function takes both the state and the action as input, while the value function only takes the state as input. This means that the Q function can be used to learn an optimal policy, while the value function can only be used to evaluate different policies. The Q function is more complex than the value function, but it can also be more accurate. The value function is simpler, but it is less accurate.
Time Line
Introduction 00:00-00:25
Definitions 00:26-01:14
Value Functions 01:15-02:56
Q-Function 02:57-04:49
Difference 04:50-5:55
#qfunction #valuefunction #reinforcementlearning #machinelearning
https://wn.com/Q_Function_And_Value_Function_Concepts_|_Reinforcement_Learning_Algorithms
Welcome to our insightful discussion on two critical concepts in the world of reinforcement learning: the Value Function and the Q-Function. These functions play a key role in estimating the expected cumulative reward in different scenarios, but they do so through distinct approaches.
Throughout this discussion, we'll delve into the nuances of these functions and explore their key differences, providing you with a deeper understanding of their significance in the realm of reinforcement learning. Let's embark on this journey of discovery together!
Value function
1) The value function estimates the expected cumulative reward of being in a particular state.
2) It is a state function, meaning that it only takes the state as input.
3) The value function can be used to evaluate different policies, and to find the optimal policy.
Q-function
1) The Q function estimates the expected cumulative reward of taking a particular action in a given state.
2) It is a state-action function, meaning that it takes both the state and the action as input.
3) The Q function is used to learn an optimal policy, which is a policy that maximizes the expected cumulative reward.
Key Difference between Q-Function and Value Function
The Q function and the value function are both used to estimate the expected cumulative reward, but they do so in different ways. The Q function takes both the state and the action as input, while the value function only takes the state as input. This means that the Q function can be used to learn an optimal policy, while the value function can only be used to evaluate different policies. The Q function is more complex than the value function, but it can also be more accurate. The value function is simpler, but it is less accurate.
Time Line
Introduction 00:00-00:25
Definitions 00:26-01:14
Value Functions 01:15-02:56
Q-Function 02:57-04:49
Difference 04:50-5:55
#qfunction #valuefunction #reinforcementlearning #machinelearning
- published: 23 Jul 2023
- views: 29
26:03
Reinforcement Learning: Machine Learning Meets Control Theory
Reinforcement learning is a powerful technique at the intersection of machine learning and control theory, and it is inspired by how biological systems learn to...
Reinforcement learning is a powerful technique at the intersection of machine learning and control theory, and it is inspired by how biological systems learn to interact with their environment. In this video, we provide a high level overview of reinforcement learning, along with leading algorithms and impressive applications.
Citable link for this video: https://doi.org/10.52843/cassyni.x2t0sp
@eigensteve on Twitter
eigensteve.com
databookuw.com
This video was produced at the University of Washington
%%% CHAPTERS %%%
0:00 Introduction
3:34 Reinforcement Learning Overview
7:30 Mathematics of Reinforcement Learning
12:32 Markov Decision Process
13:33 Credit Assignment Problem
15:38 Optimization Techniques for RL
18:54 Examples of Reinforcement Learning
21:50 Q-Learning
23:53 Hindsight Replay
https://wn.com/Reinforcement_Learning_Machine_Learning_Meets_Control_Theory
Reinforcement learning is a powerful technique at the intersection of machine learning and control theory, and it is inspired by how biological systems learn to interact with their environment. In this video, we provide a high level overview of reinforcement learning, along with leading algorithms and impressive applications.
Citable link for this video: https://doi.org/10.52843/cassyni.x2t0sp
@eigensteve on Twitter
eigensteve.com
databookuw.com
This video was produced at the University of Washington
%%% CHAPTERS %%%
0:00 Introduction
3:34 Reinforcement Learning Overview
7:30 Mathematics of Reinforcement Learning
12:32 Markov Decision Process
13:33 Credit Assignment Problem
15:38 Optimization Techniques for RL
18:54 Examples of Reinforcement Learning
21:50 Q-Learning
23:53 Hindsight Replay
- published: 12 Feb 2021
- views: 258321