Deep Web Poker
- This is the browser that initially opened the gates for dark web browsing, allowing users both high security and anonymity when using the deep and dark web. Even though it was hacked by the FBI back in 2014, it came back twice as secure and it is still the most popular choice of browsers for accessing the dark web.
- I am a professional poker player so I am curious. I know the deep web is filled with infinite amounts of crazy shit, but that stuff is pretty horrifying. I was wondering are there any poker forums on the deep web? Stuff like a pocketfives.com or twoplustwo.com but much more private and cool. Anything and everything will help.
This promotion Deep Web Poker is limited to one account per customer. Minimum deposit is €10. Players must wager the bonus amount 45 times prior Deep Web Poker to making any withdrawals. Game weighting rules apply. Country restrictions apply. 18+, New Players Only. Play Responsibly - Wagering Requirements.
- Consolidate all of your sources into a single search whether internal, public or subscription.
- Capture critical information from hundreds of digital Deep Web sources.
- Access content in real-time to stay competitive and up-to-date on new information.
- Integrate ExploritEverywhere! on your intranet, group portal, library guide or website.
- Search from any device.
Learn more about Explorit Everywhere!“I planned on spending a full day researching solar power efficiencies. With Science.gov, I found everything I needed in 20 minutes.”-Eva Abolina, International Electric Power, Pittsburgh, PA
Expert-Level Artificial Intelligence in Heads-Up No-Limit Poker
Links
Twitch YouTube Twitter
Downloads & Videos Media Contact
DeepStack bridges the gap between AI techniques for games of perfect information—like checkers, chess and Go—with ones for imperfect information games–like poker–to reason while it plays using “intuition” honed through deep learning to reassess its strategy with each decision.
With a study completed in December 2016 and published in Science in March 2017, DeepStack became the first AI capable of beating professional poker players at heads-up no-limit Texas hold'em poker.
DeepStack computes a strategy based on the current state of the game for only the remainder of the hand, not maintaining one for the full game, which leads to lower overall exploitability.
DeepStack avoids reasoning about the full remaining game by substituting computation beyond a certain depth with a fast-approximate estimate. Automatically trained with deep learning, DeepStack's “intuition” gives a gut feeling of the value of holding any cards in any situation.
DeepStack considers a reduced number of actions, allowing it to play at conventional human speeds. The system re-solves games in under five seconds using a simple gaming laptop with an Nvidia GPU.
The first computer program to outplay human professionals at heads-up no-limit Hold'em poker
In a study completed December 2016 and involving 44,000 hands of poker, DeepStack defeated 11 professional poker players with only one outside the margin of statistical significance. Over all games played, DeepStack won 49 big blinds/100 (always folding would only lose 75 bb/100), over four standard deviations from zero, making it the first computer program to beat professional poker players in heads-up no-limit Texas hold'em poker.
Games are serious business
Don’t let the name fool you, “games” of imperfect information provide a general mathematical model that describes how decision-makers interact. AI research has a long history of using parlour games to study these models, but attention has been focused primarily on perfect information games, like checkers, chess or go. Poker is the quintessential game of imperfect information, where you and your opponent hold information that each other doesn't have (your cards).
Until now, competitive AI approaches in imperfect information games have typically reasoned about the entire game, producing a complete strategy prior to play. However, to make this approach feasible in heads-up no-limit Texas hold’em—a game with vastly more unique situations than there are atoms in the universe—a simplified abstraction of the game is often needed.
A fundamentally different approach
DeepStack is the first theoretically sound application of heuristic search methods—which have been famously successful in games like checkers, chess, and Go—to imperfect information games.
At the heart of DeepStack is continual re-solving, a sound local strategy computation that only considers situations as they arise during play. This lets DeepStack avoid computing a complete strategy in advance, skirting the need for explicit abstraction.
:max_bytes(150000):strip_icc()/Pokerchips-590f911d3df78c9283b5434d.jpg "Web")
During re-solving, DeepStack doesn’t need to reason about the entire remainder of the game because it substitutes computation beyond a certain depth with a fast approximate estimate, DeepStack’s 'intuition' – a gut feeling of the value of holding any possible private cards in any possible poker situation.
Finally, DeepStack’s intuition, much like human intuition, needs to be trained. We train it with deep learning using examples generated from random poker situations.
DeepStack is theoretically sound, produces strategies substantially more difficult to exploit than abstraction-based techniques and defeats professional poker players at heads-up no-limit poker with statistical significance.
Download
Paper & Supplements
Hand Histories
Members (Front-back)
Michael Bowling, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, Viliam Lisý, Martin Schmid, Matej Moravčík, Neil Burch
Deep Web Portal
low-variance Evaluation
The performance of DeepStack and its opponents was evaluated using AIVAT, a provably unbiased low-variance technique based on carefully constructed control variates. Thanks to this technique, which gives an unbiased performance estimate with 85% reduction in standard deviation, we can show statistical significance in matches with as few as 3,000 games.
Abstraction-based Approaches
Despite using ideas from abstraction, DeepStack is fundamentally different from abstraction-based approaches, which compute and store a strategy prior to play. While DeepStack restricts the number of actions in its lookahead trees, it has no need for explicit abstraction as each re-solve starts from the actual public state, meaning DeepStack always perfectly understands the current situation.
Professional Matches
We evaluated DeepStack by playing it against a pool of professional poker players recruited by the International Federation of Poker. 44,852 games were played by 33 players from 17 countries. Eleven players completed the requested 3,000 games with DeepStack beating all but one by a statistically-significant margin. Over all games played, DeepStack outperformed players by over four standard deviations from zero.
Heuristic Search
Deep Web Percentage
At a conceptual level, DeepStack’s continual re-solving, “intuitive” local search and sparse lookahead trees describe heuristic search, which is responsible for many AI successes in perfect information games. Until DeepStack, no theoretically sound application of heuristic search was known in imperfect information games.
Deep Web Links
