-
This Superhuman Poker AI Was Trained in 20 Hours!
❤️ Check out Weights & Biases here and sign up for a free demo:
https://www.wandb.com/papers
Weights & Biases blog post with the 1 line of code visualization: https://www.wandb.com/articles/visualize-keras-models-with-one-line-of-code
📝 The paper "Superhuman AI for multiplayer poker" is available here:
- https://ai.facebook.com/blog/pluribus-first-ai-to-beat-pros-in-6-player-poker/
- https://www.cs.cmu.edu/~noamb/papers/19-Science-Superhuman.pdf
- https://science.sciencemag.org/content/early/2019/07/10/science.aay2400
🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
313V, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Anthony Vdovitchenko, Brian Gilman, Bruno Brito, Bryan Learn, Christian Ahlin, Christoph Jadanowski, Claudio Fernandes, Dani...
published: 12 Aug 2019
-
Real Time Assistance live in Action! What is RTA and why is this so dangerous for Online Poker?
Real Time Assistance / Real Time Solver (RTA, RTS) in action. Also what is RTA and why is it unfair?
In this video I will explain what Real time Assistence is and why its hurting the poker economy so much. I will also show you a Real Time Solver live at work.
Links:
https://youtu.be/_p2qqc3_UQ0
My Social Media:
https://twitter.com/praios_82
https://www.twitch.tv/praios82
#poker # casino #gambling #pokerstrategy
Crypto Donations
BTC
3LxRMXPdTEhJkTA73G5G18Fvhsn7cyzxd1
ETH
0x2D289d8141887b3E0425A032F16d5EEA69f97499
LTC
MRjM3x6yWDjkamKqiJ6rDEhPjFMZ6KHGSG
ERC20 Token
0x2D289d8141887b3E0425A032F16d5EEA69f97499
published: 21 Oct 2020
-
Deep Algo Tutorial #1 - Poker Game Algorithms
WARNING : This video is not up to date!
If you want to see the last version of Deep Algo live : ask a free demo on DeepAlgo.com
Deep Algo is a SaaS platform based on 100% automatic algorithm extraction technology.
This Tutorial explains how you can Understand the source code of a poker game.
published: 22 Jul 2016
-
Evan Medeiros: From Poker To Algorithmic Trading
Dive into the intriguing world of Evan Medeiros, where the strategic mindset of a poker player meets the analytical rigor of algorithmic trading. In this podcast, we explore Evan's unique journey from the poker table to the stock market, unraveling how his skills in risk management and psychology translated into success in the high-stakes realm of trading. Discover the parallels between poker strategies and market analysis, and learn how Evan harnessed his experience to become a prominent figure in the trading community, founding The Trade Risk. Join us for an enlightening session filled with insights, lessons, and strategies that bridge the gap between two seemingly different worlds, yet share a common thread in decision-making and risk evaluation
Learn more from Evan: https://www.thetr...
published: 03 Jan 2024
-
Poker Strategy MATH Every Pro Knows
There is a lot of math that underlies a good #poker strategy, but some concepts are more important than others. Here are the 4 concepts that every serious poker pro fully understands and calculates regularly. I've also included formulas and practice so you can refine these calculations more easily.
Throughout this video, we go through representative examples and hands to demonstrate how poker #math can be used to make better decisions at the table and gain a competitive edge over your opponents. Whether you're a beginner or an experienced player looking to up your game, this video is a must-watch for anyone who wants to improve their poker #strategy.
00:00 Poker Strategy Math Intro
00:50 Pot Odds 101
04:16 Breakeven Percentage
06:13 Auto Profit
08:03 Combos And Blockers
10:24 Expected Va...
published: 21 Apr 2023
-
How Online Poker Sites Shuffle Cards | Poker RNG Explained
In this poker video we talk about how online poker sites shuffle their cards and everything there is to know about poker RNG. We will talk about real and fake RNG's and also look at pseudo random number generators and how pokerstars uses quantum random number generators to make the poker cards shuffles fair and impossible to predict.
POKER DOCUMENTARIES - https://www.youtube.com/playlist?list=PLNrINadZFtJOpwnNoicItuylgwmF5BBGQ
POKER SCAMS - https://www.youtube.com/playlist?list=PLNrINadZFtJOUS--0RLB2DHsRDZWpLoAW
NOSEBLEED POKER BATTLES - https://www.youtube.com/playlist?list=PLNrINadZFtJOh2BCbHLmLW7Z9qyb5a0OQ
Powered by VIP-Grinders.com and Bluff The Spot - Our professional online poker partners & affiliate!
Join the VIP-Grinders new Discord group (https://discord.com/invite/kQXVKBgu...
published: 29 Nov 2021
-
Poker Ranges Explained
In this video I breakdown how to look at a poker hand using ranges and how that has developed over the years. I no longer only think about my hand vs. my opponents hand, but now look at the ranges of hands we could both have in that spot. I touch briefly on counting combinations and their importance in determining ranges. Finally, I talk about how blocker's change the possible combinations an opponent could have. Coming soon we'll release some live hands I recently played and dissect the ranges.
published: 30 Apr 2018
-
How to build a poker bot (Part 1 Counterfactual Regret minimization)
Using counterfactual regret minimization you can build your own poker bot that can beat the best pros
Code: https://github.com/IanSullivan/PokerCFR
Support : https://www.buymeacoffee.com/iansul
Part 2 https://www.youtube.com/watch?v=Qz3kSJv_9mE
Final product: https://www.youtube.com/watch?v=nIcJEc_JOp0
Libratus paper https://www.cs.cmu.edu/~noamb/papers/17-IJCAI-Libratus.pdf
Solved heads up limit poker http://poker.srv.ualberta.ca/
published: 11 Oct 2020
-
How to Build a Superhuman Poker AI using CFR | Creating a Poker Bot Part 2
In the past few years, poker AIs have defeated the top poker players in the world. In this video, I discuss the Counterfactual Regret Minimization (CFR) algorithm that make superhuman poker bots possible. Be sure to check out Taskade, a great tool for project management, productivity, and collaboration: https://www.taskade.com
Game theory says that there is a Nash equilibrium in poker (meaning an "optimal" solution). In 2017, CMU's poker bot, Libratus, defeated 4 world-renowned poker players in heads up, at 99.98% statistical significance. In 2019, Pluribus, another CMU poker bot, defeated pros in 6-player No Limit Hold'em. The algorithm behind it all is from a domain of computer science called reinforcement learning. It is a self-play algorithm that learns the optimal strategy by playing...
published: 22 Mar 2021
-
ReBeL - Combining Deep Reinforcement Learning and Search for Imperfect-Information Games (Explained)
#ai #technology #poker
This paper does for Poker what AlphaZero has done for Chess & Go. The combination of Self-Play Reinforcement Learning and Tree Search has had tremendous success in perfect-information games, but transferring such techniques to imperfect information games is a hard problem. Not only does ReBeL solve this problem, but it provably converges to a Nash Equilibrium and delivers a superhuman Heads Up No-Limit Hold'em bot with very little domain knowledge.
OUTLINE:
0:00 - Intro & Overview
3:20 - Rock, Paper, and Double Scissor
10:00 - AlphaZero Tree Search
18:30 - Notation Setup: Infostates & Nash Equilibria
31:45 - One Card Poker: Introducing Belief Representations
45:00 - Solving Games in Belief Representation
55:20 - The ReBeL Algorithm
1:04:00 - Theory & Experiment Res...
published: 16 Dec 2020
5:32
This Superhuman Poker AI Was Trained in 20 Hours!
❤️ Check out Weights & Biases here and sign up for a free demo:
https://www.wandb.com/papers
Weights & Biases blog post with the 1 line of code visualization: ...
❤️ Check out Weights & Biases here and sign up for a free demo:
https://www.wandb.com/papers
Weights & Biases blog post with the 1 line of code visualization: https://www.wandb.com/articles/visualize-keras-models-with-one-line-of-code
📝 The paper "Superhuman AI for multiplayer poker" is available here:
- https://ai.facebook.com/blog/pluribus-first-ai-to-beat-pros-in-6-player-poker/
- https://www.cs.cmu.edu/~noamb/papers/19-Science-Superhuman.pdf
- https://science.sciencemag.org/content/early/2019/07/10/science.aay2400
🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
313V, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Anthony Vdovitchenko, Brian Gilman, Bruno Brito, Bryan Learn, Christian Ahlin, Christoph Jadanowski, Claudio Fernandes, Daniel Hasegan, Dennis Abts, Eric Haddad, Eric Martel, Evan Breznyik, Geronimo Moralez, James Watt, Javier Bustamante, John De Witt, Kaiesh Vohra, Kasia Hayden, Kjartan Olason, Levente Szabo, Lorin Atzberger, Lukas Biewald, Marcin Dukaczewski, Marten Rauschenberg, Maurits van Mastrigt, Michael Albrecht, Michael Jensen, Nader Shakerin, Owen Campbell-Moore, Owen Skarpness, Raul Araújo da Silva, Rob Rowe, Robin Graham, Ryan Monsurate, Shawn Azman, Steef, Steve Messina, Sunil Kim, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Zach Boldyga.
https://www.patreon.com/TwoMinutePapers
Splash screen/thumbnail design: Felícia Fehér - http://felicia.hu
Károly Zsolnai-Fehér's links:
Instagram: https://www.instagram.com/twominutepapers/
Twitter: https://twitter.com/karoly_zsolnai
Web: https://cg.tuwien.ac.at/~zsolnai/
#Poker #PokerAI
https://wn.com/This_Superhuman_Poker_Ai_Was_Trained_In_20_Hours
❤️ Check out Weights & Biases here and sign up for a free demo:
https://www.wandb.com/papers
Weights & Biases blog post with the 1 line of code visualization: https://www.wandb.com/articles/visualize-keras-models-with-one-line-of-code
📝 The paper "Superhuman AI for multiplayer poker" is available here:
- https://ai.facebook.com/blog/pluribus-first-ai-to-beat-pros-in-6-player-poker/
- https://www.cs.cmu.edu/~noamb/papers/19-Science-Superhuman.pdf
- https://science.sciencemag.org/content/early/2019/07/10/science.aay2400
🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
313V, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Anthony Vdovitchenko, Brian Gilman, Bruno Brito, Bryan Learn, Christian Ahlin, Christoph Jadanowski, Claudio Fernandes, Daniel Hasegan, Dennis Abts, Eric Haddad, Eric Martel, Evan Breznyik, Geronimo Moralez, James Watt, Javier Bustamante, John De Witt, Kaiesh Vohra, Kasia Hayden, Kjartan Olason, Levente Szabo, Lorin Atzberger, Lukas Biewald, Marcin Dukaczewski, Marten Rauschenberg, Maurits van Mastrigt, Michael Albrecht, Michael Jensen, Nader Shakerin, Owen Campbell-Moore, Owen Skarpness, Raul Araújo da Silva, Rob Rowe, Robin Graham, Ryan Monsurate, Shawn Azman, Steef, Steve Messina, Sunil Kim, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Zach Boldyga.
https://www.patreon.com/TwoMinutePapers
Splash screen/thumbnail design: Felícia Fehér - http://felicia.hu
Károly Zsolnai-Fehér's links:
Instagram: https://www.instagram.com/twominutepapers/
Twitter: https://twitter.com/karoly_zsolnai
Web: https://cg.tuwien.ac.at/~zsolnai/
#Poker #PokerAI
- published: 12 Aug 2019
- views: 474870
8:02
Real Time Assistance live in Action! What is RTA and why is this so dangerous for Online Poker?
Real Time Assistance / Real Time Solver (RTA, RTS) in action. Also what is RTA and why is it unfair?
In this video I will explain what Real time Assistence is ...
Real Time Assistance / Real Time Solver (RTA, RTS) in action. Also what is RTA and why is it unfair?
In this video I will explain what Real time Assistence is and why its hurting the poker economy so much. I will also show you a Real Time Solver live at work.
Links:
https://youtu.be/_p2qqc3_UQ0
My Social Media:
https://twitter.com/praios_82
https://www.twitch.tv/praios82
#poker # casino #gambling #pokerstrategy
Crypto Donations
BTC
3LxRMXPdTEhJkTA73G5G18Fvhsn7cyzxd1
ETH
0x2D289d8141887b3E0425A032F16d5EEA69f97499
LTC
MRjM3x6yWDjkamKqiJ6rDEhPjFMZ6KHGSG
ERC20 Token
0x2D289d8141887b3E0425A032F16d5EEA69f97499
https://wn.com/Real_Time_Assistance_Live_In_Action_What_Is_Rta_And_Why_Is_This_So_Dangerous_For_Online_Poker
Real Time Assistance / Real Time Solver (RTA, RTS) in action. Also what is RTA and why is it unfair?
In this video I will explain what Real time Assistence is and why its hurting the poker economy so much. I will also show you a Real Time Solver live at work.
Links:
https://youtu.be/_p2qqc3_UQ0
My Social Media:
https://twitter.com/praios_82
https://www.twitch.tv/praios82
#poker # casino #gambling #pokerstrategy
Crypto Donations
BTC
3LxRMXPdTEhJkTA73G5G18Fvhsn7cyzxd1
ETH
0x2D289d8141887b3E0425A032F16d5EEA69f97499
LTC
MRjM3x6yWDjkamKqiJ6rDEhPjFMZ6KHGSG
ERC20 Token
0x2D289d8141887b3E0425A032F16d5EEA69f97499
- published: 21 Oct 2020
- views: 69117
6:27
Deep Algo Tutorial #1 - Poker Game Algorithms
WARNING : This video is not up to date!
If you want to see the last version of Deep Algo live : ask a free demo on DeepAlgo.com
Deep Algo is a SaaS platform b...
WARNING : This video is not up to date!
If you want to see the last version of Deep Algo live : ask a free demo on DeepAlgo.com
Deep Algo is a SaaS platform based on 100% automatic algorithm extraction technology.
This Tutorial explains how you can Understand the source code of a poker game.
https://wn.com/Deep_Algo_Tutorial_1_Poker_Game_Algorithms
WARNING : This video is not up to date!
If you want to see the last version of Deep Algo live : ask a free demo on DeepAlgo.com
Deep Algo is a SaaS platform based on 100% automatic algorithm extraction technology.
This Tutorial explains how you can Understand the source code of a poker game.
- published: 22 Jul 2016
- views: 16635
50:06
Evan Medeiros: From Poker To Algorithmic Trading
Dive into the intriguing world of Evan Medeiros, where the strategic mindset of a poker player meets the analytical rigor of algorithmic trading. In this podcas...
Dive into the intriguing world of Evan Medeiros, where the strategic mindset of a poker player meets the analytical rigor of algorithmic trading. In this podcast, we explore Evan's unique journey from the poker table to the stock market, unraveling how his skills in risk management and psychology translated into success in the high-stakes realm of trading. Discover the parallels between poker strategies and market analysis, and learn how Evan harnessed his experience to become a prominent figure in the trading community, founding The Trade Risk. Join us for an enlightening session filled with insights, lessons, and strategies that bridge the gap between two seemingly different worlds, yet share a common thread in decision-making and risk evaluation
Learn more from Evan: https://www.thetraderisk.com/
Follow him on twitter: https://twitter.com/theTradeRisk
Watch his Youtube: https://www.youtube.com/@TradeRisk
learn to algo trade - https://algotradecamp.com/spotify
follow me on twitter - https://twitter.com/MoonDevOnYT
do you want to trade automatically? do you want to remove all emotions? do you want to be connect with and learn from other automated traders? over the next 21 days i will take you by the hand to help you automate your trading, invite you to our quant community & give you all of my trading algorithms + code. join my algo trade camp where i show you step by step how to automate your trading: https://algotradecamp.com
https://wn.com/Evan_Medeiros_From_Poker_To_Algorithmic_Trading
Dive into the intriguing world of Evan Medeiros, where the strategic mindset of a poker player meets the analytical rigor of algorithmic trading. In this podcast, we explore Evan's unique journey from the poker table to the stock market, unraveling how his skills in risk management and psychology translated into success in the high-stakes realm of trading. Discover the parallels between poker strategies and market analysis, and learn how Evan harnessed his experience to become a prominent figure in the trading community, founding The Trade Risk. Join us for an enlightening session filled with insights, lessons, and strategies that bridge the gap between two seemingly different worlds, yet share a common thread in decision-making and risk evaluation
Learn more from Evan: https://www.thetraderisk.com/
Follow him on twitter: https://twitter.com/theTradeRisk
Watch his Youtube: https://www.youtube.com/@TradeRisk
learn to algo trade - https://algotradecamp.com/spotify
follow me on twitter - https://twitter.com/MoonDevOnYT
do you want to trade automatically? do you want to remove all emotions? do you want to be connect with and learn from other automated traders? over the next 21 days i will take you by the hand to help you automate your trading, invite you to our quant community & give you all of my trading algorithms + code. join my algo trade camp where i show you step by step how to automate your trading: https://algotradecamp.com
- published: 03 Jan 2024
- views: 997
16:47
Poker Strategy MATH Every Pro Knows
There is a lot of math that underlies a good #poker strategy, but some concepts are more important than others. Here are the 4 concepts that every serious poker...
There is a lot of math that underlies a good #poker strategy, but some concepts are more important than others. Here are the 4 concepts that every serious poker pro fully understands and calculates regularly. I've also included formulas and practice so you can refine these calculations more easily.
Throughout this video, we go through representative examples and hands to demonstrate how poker #math can be used to make better decisions at the table and gain a competitive edge over your opponents. Whether you're a beginner or an experienced player looking to up your game, this video is a must-watch for anyone who wants to improve their poker #strategy.
00:00 Poker Strategy Math Intro
00:50 Pot Odds 101
04:16 Breakeven Percentage
06:13 Auto Profit
08:03 Combos And Blockers
10:24 Expected Value (EV)
16:06 Conclusion
*PRACTICE YOUR POKER MATH*
https://www.splitsuit.com/poker-preflop-math-workbook
*RELATED CONTENT*
· Free Pot Odds Calculator: https://www.splitsuit.com/free-pot-odds-poker-tool
· Poker EV Formula: https://www.splitsuit.com/simple-poker-expected-value-formula
· Poker Combos & Blockers: https://www.splitsuit.com/poker-combos-blockers
· The Postflop Workbook: https://www.splitsuit.com/postflop-poker-workbook
https://wn.com/Poker_Strategy_Math_Every_Pro_Knows
There is a lot of math that underlies a good #poker strategy, but some concepts are more important than others. Here are the 4 concepts that every serious poker pro fully understands and calculates regularly. I've also included formulas and practice so you can refine these calculations more easily.
Throughout this video, we go through representative examples and hands to demonstrate how poker #math can be used to make better decisions at the table and gain a competitive edge over your opponents. Whether you're a beginner or an experienced player looking to up your game, this video is a must-watch for anyone who wants to improve their poker #strategy.
00:00 Poker Strategy Math Intro
00:50 Pot Odds 101
04:16 Breakeven Percentage
06:13 Auto Profit
08:03 Combos And Blockers
10:24 Expected Value (EV)
16:06 Conclusion
*PRACTICE YOUR POKER MATH*
https://www.splitsuit.com/poker-preflop-math-workbook
*RELATED CONTENT*
· Free Pot Odds Calculator: https://www.splitsuit.com/free-pot-odds-poker-tool
· Poker EV Formula: https://www.splitsuit.com/simple-poker-expected-value-formula
· Poker Combos & Blockers: https://www.splitsuit.com/poker-combos-blockers
· The Postflop Workbook: https://www.splitsuit.com/postflop-poker-workbook
- published: 21 Apr 2023
- views: 191576
8:48
How Online Poker Sites Shuffle Cards | Poker RNG Explained
In this poker video we talk about how online poker sites shuffle their cards and everything there is to know about poker RNG. We will talk about real and fake R...
In this poker video we talk about how online poker sites shuffle their cards and everything there is to know about poker RNG. We will talk about real and fake RNG's and also look at pseudo random number generators and how pokerstars uses quantum random number generators to make the poker cards shuffles fair and impossible to predict.
POKER DOCUMENTARIES - https://www.youtube.com/playlist?list=PLNrINadZFtJOpwnNoicItuylgwmF5BBGQ
POKER SCAMS - https://www.youtube.com/playlist?list=PLNrINadZFtJOUS--0RLB2DHsRDZWpLoAW
NOSEBLEED POKER BATTLES - https://www.youtube.com/playlist?list=PLNrINadZFtJOh2BCbHLmLW7Z9qyb5a0OQ
Powered by VIP-Grinders.com and Bluff The Spot - Our professional online poker partners & affiliate!
Join the VIP-Grinders new Discord group (https://discord.com/invite/kQXVKBguch) to get access to $8,000 in exclusive monthly freerolls and try online poker for free!
Visit https://www.vip-grinders.com/go/poker-bounty for more information on our rakeback deals and services
Use discount code "BOUNTY20" for a 20% discount on BTS courses
* BTS Academy - https://www.bluffthespot.com/bts-Academy
* BTS Ultimate Course - https://www.bluffthespot.com/whats-in-the-ultimate-course-2
MMAsherdog Biggest hands at NL40k - https://www.youtube.com/watch?v=TsQCNspmJ3o&list=PLYohDo5aDR1drtztupH7ct0fITvNATowS&index=24
Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing.
https://wn.com/How_Online_Poker_Sites_Shuffle_Cards_|_Poker_Rng_Explained
In this poker video we talk about how online poker sites shuffle their cards and everything there is to know about poker RNG. We will talk about real and fake RNG's and also look at pseudo random number generators and how pokerstars uses quantum random number generators to make the poker cards shuffles fair and impossible to predict.
POKER DOCUMENTARIES - https://www.youtube.com/playlist?list=PLNrINadZFtJOpwnNoicItuylgwmF5BBGQ
POKER SCAMS - https://www.youtube.com/playlist?list=PLNrINadZFtJOUS--0RLB2DHsRDZWpLoAW
NOSEBLEED POKER BATTLES - https://www.youtube.com/playlist?list=PLNrINadZFtJOh2BCbHLmLW7Z9qyb5a0OQ
Powered by VIP-Grinders.com and Bluff The Spot - Our professional online poker partners & affiliate!
Join the VIP-Grinders new Discord group (https://discord.com/invite/kQXVKBguch) to get access to $8,000 in exclusive monthly freerolls and try online poker for free!
Visit https://www.vip-grinders.com/go/poker-bounty for more information on our rakeback deals and services
Use discount code "BOUNTY20" for a 20% discount on BTS courses
* BTS Academy - https://www.bluffthespot.com/bts-Academy
* BTS Ultimate Course - https://www.bluffthespot.com/whats-in-the-ultimate-course-2
MMAsherdog Biggest hands at NL40k - https://www.youtube.com/watch?v=TsQCNspmJ3o&list=PLYohDo5aDR1drtztupH7ct0fITvNATowS&index=24
Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing.
- published: 29 Nov 2021
- views: 19874
11:31
Poker Ranges Explained
In this video I breakdown how to look at a poker hand using ranges and how that has developed over the years. I no longer only think about my hand vs. my oppone...
In this video I breakdown how to look at a poker hand using ranges and how that has developed over the years. I no longer only think about my hand vs. my opponents hand, but now look at the ranges of hands we could both have in that spot. I touch briefly on counting combinations and their importance in determining ranges. Finally, I talk about how blocker's change the possible combinations an opponent could have. Coming soon we'll release some live hands I recently played and dissect the ranges.
https://wn.com/Poker_Ranges_Explained
In this video I breakdown how to look at a poker hand using ranges and how that has developed over the years. I no longer only think about my hand vs. my opponents hand, but now look at the ranges of hands we could both have in that spot. I touch briefly on counting combinations and their importance in determining ranges. Finally, I talk about how blocker's change the possible combinations an opponent could have. Coming soon we'll release some live hands I recently played and dissect the ranges.
- published: 30 Apr 2018
- views: 3323012
8:52
How to build a poker bot (Part 1 Counterfactual Regret minimization)
Using counterfactual regret minimization you can build your own poker bot that can beat the best pros
Code: https://github.com/IanSullivan/PokerCFR
Support : ...
Using counterfactual regret minimization you can build your own poker bot that can beat the best pros
Code: https://github.com/IanSullivan/PokerCFR
Support : https://www.buymeacoffee.com/iansul
Part 2 https://www.youtube.com/watch?v=Qz3kSJv_9mE
Final product: https://www.youtube.com/watch?v=nIcJEc_JOp0
Libratus paper https://www.cs.cmu.edu/~noamb/papers/17-IJCAI-Libratus.pdf
Solved heads up limit poker http://poker.srv.ualberta.ca/
https://wn.com/How_To_Build_A_Poker_Bot_(Part_1_Counterfactual_Regret_Minimization)
Using counterfactual regret minimization you can build your own poker bot that can beat the best pros
Code: https://github.com/IanSullivan/PokerCFR
Support : https://www.buymeacoffee.com/iansul
Part 2 https://www.youtube.com/watch?v=Qz3kSJv_9mE
Final product: https://www.youtube.com/watch?v=nIcJEc_JOp0
Libratus paper https://www.cs.cmu.edu/~noamb/papers/17-IJCAI-Libratus.pdf
Solved heads up limit poker http://poker.srv.ualberta.ca/
- published: 11 Oct 2020
- views: 25339
14:00
How to Build a Superhuman Poker AI using CFR | Creating a Poker Bot Part 2
In the past few years, poker AIs have defeated the top poker players in the world. In this video, I discuss the Counterfactual Regret Minimization (CFR) algorit...
In the past few years, poker AIs have defeated the top poker players in the world. In this video, I discuss the Counterfactual Regret Minimization (CFR) algorithm that make superhuman poker bots possible. Be sure to check out Taskade, a great tool for project management, productivity, and collaboration: https://www.taskade.com
Game theory says that there is a Nash equilibrium in poker (meaning an "optimal" solution). In 2017, CMU's poker bot, Libratus, defeated 4 world-renowned poker players in heads up, at 99.98% statistical significance. In 2019, Pluribus, another CMU poker bot, defeated pros in 6-player No Limit Hold'em. The algorithm behind it all is from a domain of computer science called reinforcement learning. It is a self-play algorithm that learns the optimal strategy by playing against itself. The Counterfactual Regret Minimization (CFR) algorithm decides which decisions to make based off where it might minimize the most regret. In this video, I explain how this algorithm works!
Some of you might want to code your own poker bot. Some of you might be working on other projects. Either way, you should use Taskade! It's a great tool for managing projects and being super productive. It's also awesome for streamlining tasks and keeping track of collaboration. Best of all, it is simple to use and free!!! Check it out here: https://www.taskade.com
Timestamps:
0:00 Intro
0:56 Reinforcement Learning
2:34 Basic Idea of CFR
4:04 Game Tree and Regret
7:27 Creating Abstractions
11:38 Putting It Together
12:33 Superhuman AI Performance
Papers/Resources:
http://modelai.gettysburg.edu/2013/cfr/cfr.pdf
https://papers.nips.cc/paper/2012/file/3df1d4b96d8976ff5986393e8767f5b2-Paper.pdf
https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.295.2143&rep=rep1&type=pdf
https://int8.io/counterfactual-regret-minimization-for-poker-ai/
https://icml.cc/media/Slides/icml/2019/102(11-11-00)-11-12-15-4443-deep_counterfac.pdf
Feel free to leave any questions.
Please consider subscribing if you liked this video: https://www.youtube.com/c/ycubed?sub_confirmation=1
Thanks for watching everyone!
~~~~~~~~~~~~~~~~~~~~~~~~
Follow me on Instagram: https://www.instagram.com/kylieyying
Follow me on Twitter: https://www.twitter.com/kylieyying
Check out my website: https://www.kylieying.com
https://wn.com/How_To_Build_A_Superhuman_Poker_Ai_Using_Cfr_|_Creating_A_Poker_Bot_Part_2
In the past few years, poker AIs have defeated the top poker players in the world. In this video, I discuss the Counterfactual Regret Minimization (CFR) algorithm that make superhuman poker bots possible. Be sure to check out Taskade, a great tool for project management, productivity, and collaboration: https://www.taskade.com
Game theory says that there is a Nash equilibrium in poker (meaning an "optimal" solution). In 2017, CMU's poker bot, Libratus, defeated 4 world-renowned poker players in heads up, at 99.98% statistical significance. In 2019, Pluribus, another CMU poker bot, defeated pros in 6-player No Limit Hold'em. The algorithm behind it all is from a domain of computer science called reinforcement learning. It is a self-play algorithm that learns the optimal strategy by playing against itself. The Counterfactual Regret Minimization (CFR) algorithm decides which decisions to make based off where it might minimize the most regret. In this video, I explain how this algorithm works!
Some of you might want to code your own poker bot. Some of you might be working on other projects. Either way, you should use Taskade! It's a great tool for managing projects and being super productive. It's also awesome for streamlining tasks and keeping track of collaboration. Best of all, it is simple to use and free!!! Check it out here: https://www.taskade.com
Timestamps:
0:00 Intro
0:56 Reinforcement Learning
2:34 Basic Idea of CFR
4:04 Game Tree and Regret
7:27 Creating Abstractions
11:38 Putting It Together
12:33 Superhuman AI Performance
Papers/Resources:
http://modelai.gettysburg.edu/2013/cfr/cfr.pdf
https://papers.nips.cc/paper/2012/file/3df1d4b96d8976ff5986393e8767f5b2-Paper.pdf
https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.295.2143&rep=rep1&type=pdf
https://int8.io/counterfactual-regret-minimization-for-poker-ai/
https://icml.cc/media/Slides/icml/2019/102(11-11-00)-11-12-15-4443-deep_counterfac.pdf
Feel free to leave any questions.
Please consider subscribing if you liked this video: https://www.youtube.com/c/ycubed?sub_confirmation=1
Thanks for watching everyone!
~~~~~~~~~~~~~~~~~~~~~~~~
Follow me on Instagram: https://www.instagram.com/kylieyying
Follow me on Twitter: https://www.twitter.com/kylieyying
Check out my website: https://www.kylieying.com
- published: 22 Mar 2021
- views: 40557
1:12:22
ReBeL - Combining Deep Reinforcement Learning and Search for Imperfect-Information Games (Explained)
#ai #technology #poker
This paper does for Poker what AlphaZero has done for Chess & Go. The combination of Self-Play Reinforcement Learning and Tree Search ha...
#ai #technology #poker
This paper does for Poker what AlphaZero has done for Chess & Go. The combination of Self-Play Reinforcement Learning and Tree Search has had tremendous success in perfect-information games, but transferring such techniques to imperfect information games is a hard problem. Not only does ReBeL solve this problem, but it provably converges to a Nash Equilibrium and delivers a superhuman Heads Up No-Limit Hold'em bot with very little domain knowledge.
OUTLINE:
0:00 - Intro & Overview
3:20 - Rock, Paper, and Double Scissor
10:00 - AlphaZero Tree Search
18:30 - Notation Setup: Infostates & Nash Equilibria
31:45 - One Card Poker: Introducing Belief Representations
45:00 - Solving Games in Belief Representation
55:20 - The ReBeL Algorithm
1:04:00 - Theory & Experiment Results
1:07:00 - Broader Impact
1:10:20 - High-Level Summary
Paper: https://arxiv.org/abs/2007.13544
Code: https://github.com/facebookresearch/rebel
Blog: https://ai.facebook.com/blog/rebel-a-general-game-playing-ai-bot-that-excels-at-poker-and-more/
ERRATA: As someone last video pointed out: This is not the best Poker algorithm, but the best one that uses very little expert knowledge.
Abstract:
The combination of deep reinforcement learning and search at both training and test time is a powerful paradigm that has led to a number of successes in single-agent settings and perfect-information games, best exemplified by AlphaZero. However, prior algorithms of this form cannot cope with imperfect-information games. This paper presents ReBeL, a general framework for self-play reinforcement learning and search that provably converges to a Nash equilibrium in any two-player zero-sum game. In the simpler setting of perfect-information games, ReBeL reduces to an algorithm similar to AlphaZero. Results in two different imperfect-information games show ReBeL converges to an approximate Nash equilibrium. We also show ReBeL achieves superhuman performance in heads-up no-limit Texas hold'em poker, while using far less domain knowledge than any prior poker AI.
Authors: Noam Brown, Anton Bakhtin, Adam Lerer, Qucheng Gong
Links:
YouTube: https://www.youtube.com/c/yannickilcher
Twitter: https://twitter.com/ykilcher
Discord: https://discord.gg/4H8xxDF
BitChute: https://www.bitchute.com/channel/yannic-kilcher
Minds: https://www.minds.com/ykilcher
Parler: https://parler.com/profile/YannicKilcher
LinkedIn: https://www.linkedin.com/in/yannic-kilcher-488534136/
If you want to support me, the best thing to do is to share out the content :)
If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this):
SubscribeStar: https://www.subscribestar.com/yannickilcher
Patreon: https://www.patreon.com/yannickilcher
Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq
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https://wn.com/Rebel_Combining_Deep_Reinforcement_Learning_And_Search_For_Imperfect_Information_Games_(Explained)
#ai #technology #poker
This paper does for Poker what AlphaZero has done for Chess & Go. The combination of Self-Play Reinforcement Learning and Tree Search has had tremendous success in perfect-information games, but transferring such techniques to imperfect information games is a hard problem. Not only does ReBeL solve this problem, but it provably converges to a Nash Equilibrium and delivers a superhuman Heads Up No-Limit Hold'em bot with very little domain knowledge.
OUTLINE:
0:00 - Intro & Overview
3:20 - Rock, Paper, and Double Scissor
10:00 - AlphaZero Tree Search
18:30 - Notation Setup: Infostates & Nash Equilibria
31:45 - One Card Poker: Introducing Belief Representations
45:00 - Solving Games in Belief Representation
55:20 - The ReBeL Algorithm
1:04:00 - Theory & Experiment Results
1:07:00 - Broader Impact
1:10:20 - High-Level Summary
Paper: https://arxiv.org/abs/2007.13544
Code: https://github.com/facebookresearch/rebel
Blog: https://ai.facebook.com/blog/rebel-a-general-game-playing-ai-bot-that-excels-at-poker-and-more/
ERRATA: As someone last video pointed out: This is not the best Poker algorithm, but the best one that uses very little expert knowledge.
Abstract:
The combination of deep reinforcement learning and search at both training and test time is a powerful paradigm that has led to a number of successes in single-agent settings and perfect-information games, best exemplified by AlphaZero. However, prior algorithms of this form cannot cope with imperfect-information games. This paper presents ReBeL, a general framework for self-play reinforcement learning and search that provably converges to a Nash equilibrium in any two-player zero-sum game. In the simpler setting of perfect-information games, ReBeL reduces to an algorithm similar to AlphaZero. Results in two different imperfect-information games show ReBeL converges to an approximate Nash equilibrium. We also show ReBeL achieves superhuman performance in heads-up no-limit Texas hold'em poker, while using far less domain knowledge than any prior poker AI.
Authors: Noam Brown, Anton Bakhtin, Adam Lerer, Qucheng Gong
Links:
YouTube: https://www.youtube.com/c/yannickilcher
Twitter: https://twitter.com/ykilcher
Discord: https://discord.gg/4H8xxDF
BitChute: https://www.bitchute.com/channel/yannic-kilcher
Minds: https://www.minds.com/ykilcher
Parler: https://parler.com/profile/YannicKilcher
LinkedIn: https://www.linkedin.com/in/yannic-kilcher-488534136/
If you want to support me, the best thing to do is to share out the content :)
If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this):
SubscribeStar: https://www.subscribestar.com/yannickilcher
Patreon: https://www.patreon.com/yannickilcher
Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq
Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2
Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m
Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n
- published: 16 Dec 2020
- views: 34438