🤑 Python library for teaching TensorFlow neural nets to play Blackjack and count cards_Github - jishuwen(技术文)

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neural networks - Different action spaces for different states of the environment - Artificial Intelligence Stack Exchange
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For the player you should definitively implement what blackjack players call. Maybe you can train a neural net (or some other estimating ...


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Python library for teaching TensorFlow neural nets to play Blackjack and count cards_Github - jishuwen(技术文)
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Blackjack as a Test Bed for Learning Strategies in Neural Networks. A. Perez-Uribe and E. Sanchez. Swiss Federal Institute of Technology. IEEE IJCNN'98.


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When looking through the environments I came across "Blackjack-v0" which is a basic implementation of the game where blackjack neural network state is the hand count of the player blackjack neural network the dealer and if the player has an useable ace.
The actions are only hit or stand and the possible rewards 1 if the player wins, -1 if the player loses and 0 when draw.
But now there are two different action spaces which apply to different states of the game betting or playingso some of the actions are useless.
How would be the right way to approach this scenario?
I'm guessing one answer would be to give some kind of negative reward if the network guesses an useless action but in this case I think the reward should be the actual stake negative reward and the actual win if any.
Therefor this would cause some bias in how the game proceeds as it should start with some amount of balance and end if the balance is 0 or after a specified amount of rounds.
Limiting timesteps wouldn't be an option either I guess because it should be limited to rounds so it won't end after a betting step e.
Therefore, for a useless step the reward would be 0 and the state would stay blackjack neural network same but for the network it doesn't matter how many useless steps it takes because it'll make no difference to the actual outcome.
One for betting and one for playing?
I took the liberty of massaging your question slightly, and adding the "ai-basics" tag.
Always happy to assist.
Don't be blackjacks and grill pa about asking any informal questions on or.
A NN can get state and action as an input and expected discounted reward as an output.
But you can just not pick an action if this action blackjack 777 payout available and there isn't necessity to give it negative or zero reward just don't learn your net to change it behavior for these actions because they are neither good nor bad.
The problem see more, that neural networks needs something against which they can be modeled, neural networks alone are nothing and everything.
Before a neural network can be implemented in Torch the underlying strategy must be programmed in normal sourcecode.
The easiest way in doing so is to observe the actions of blackjack neural network human player.
A game log is used, which is parsed by the blackjack-strategy model programmed in sourcecode not as neural network and only for the second step dealing with uncertainty a DQN neural network can be used.
Let us go into the details.
In neural network based gameplaying there are two options available: model-free learning and model-based learning.
The second one is easier for beginner.
The model a blackjack Game AI is programmed with traditional programming techniques like Behavior trees, and the neural network acts as a helper modul for deciding one parameter in the hand-crafted model.
For example, if in the sourcecode a variable is unknown and must adjusted to optimize a certain goal, then the neural network can help to find the parameter.
In contrast, so called model free reinforcement learning is very difficult to implement.
That would be equal to start with no prior knowledge and let the network learn everything.
That is often done with NEAT neuroevolutuion but i would guess, that Blackjack is a too complicated domain to learn the complete model from scratch.
I wanted to do model free learning as I wanted to see what kind of tactics the AI finds out https://clearadultskin.com/blackjack/basic-blackjack-strategy-mit.html it's own without any guidings.
Since I couldn't really figure out described problem I decided to go with multiple networks for multiple actions.
So I wrote an environment similar to open ai blackjack neural network, with 5 different actions bet, insure, split, double, hit at different timesteps.
I use a DQN openai baselines as network for each of these actions.
Maybe that wouldn't make any difference or even bias decisions but I'm not sure.
Provide details and share your research!
Use MathJax to format equations.
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Abstract- In this paper we investigate the evolution of a blackjack player. We utilise three neural networks (one for splitting, one for doubling down and one for ...


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Eugene Nho: Whiskey and Blackjack — What Machine Learning Teaches Humans about Learning

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neural networks - Different action spaces for different states of the environment - Artificial Intelligence Stack Exchange
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Abstract: Blackjack or twenty-one is a card game where the player attempts to beat the dealer, by obtaining a sum of card values that is equal to or less than 21 ...


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The evolution of blackjack strategies | Semantic Scholar
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A project management ecosystem - milestoen
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This is done by generating random hands, letting the computer make random moves, and storing representations of the hands tagged with the eventual outcome of the decision.
The third blackjack neural network is the level of information to put in the dataset.
Level 1 stores only information about the players hand value.
Level 2 stores level 1 plus the dealers face-up card.
Level 3 stores level 2 plus a record of all cards seen.
For the purpose of training a nuerel network to play blackjack, we want to represent a hand in a way that tells us whether we should 'hit' or 'stay.
We then tag the data as either 'h' or 's' for 'hit' or 'stay.
A data set for this task was produced with 3,959 monte carlo simulations generated with Blackjack.
The first layer contained 4096 neurons, while the second only had two, for 'hit' or 'stay.
There are 10 epochs.
This happens to be the strategy used by the dealer.
Second Blackjack model - data set level 2 This model will use all the previous techniques, but the data set will now include the dealer's upward facing card.
Loading the data is done the same as in model 1.
The optimizer was blackjack neural network and there were 100 epochs.
For testing purposes I found this nifty chart for Blackjack strategy at wizardofodds.
There is a clear pattern on both.
This confirms the neural network has begun to learn the strategy of Blackjack.
The next model with contain information on which blackjack neural network have been seen throughout the buenos blackjack casino aires, blackjack neural network that the model will learn to count cards.
Third Blackjack Model - data set level 3 This model will use the same data as prevous models, but now it will also contain a record of every card so far seen.
The simulation implies the dealer is using a single deck until it runs out of cards, and then reshuffles them.
The optimizer was 'adam' and there were 50 epochs.
Testing of this model has not yet been source />Please check back for this feature.

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... just don't learn your net to change it behavior for these actions because they are. the OP is trying to modeling a blackjack strategy with neural networks and ...


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A project management ecosystem - milestoen
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Jul 11 2017 Categories: ~ 4 min read.
There seems to be a lot of disparate information about project management out there.
PMI project management institute is doing an awesome job with the certification process but the sad truth we all know those who are PMP certified is we study concepts and processes and memorize them to pass the exam knowing that they make no sense in real life scenarios.
The temporary nature of projects indicates that a project has a definite beginning and end.
Equation The rationale falls short with mentioning that a project in general has finite resources at its disposal and those are budget, timeframe, and a clear scope of work.
These 3 elements constitute the equation a PM has road galena continuously balance.
So in our case, awareness of the local event in question.
Value changes over time as the project starts to see the light and therefore it blackjack neural network important to continuously ensure project objectives are aligned to the business case.
So Value is a function of budget, time and scope and maybe quality of work.
As well, lately a considerable number of our clients are moving towards outcomes based engagements; meaning that we as a project team are remunerated based on the outcome we produce on projects.
Elements In order to determine how the work is done, there are 3 elements to consider; although we are developing a unique output, there is a 1 process to follow blackjack neural network the 2 people with specific source sets having the right 3 tools at their disposal.
Think of it as a science and an art — the science is the quantifiable or tangible part and the art is more on the intangible or qualitative side.
Methodologies There is no one size fits all for project methodologies or systems.
In most cases, teams choose to use a hybrid of several methods depending on the size, nature and complexity of the engagement.
The most common are Agile and Waterfall.
Frequent delivery blackjack neural network to ensure visibility of progress, creating opportunities for real -time feedback and changes in scope throughout the life-cycle.
Phases can lasts for weeks or months.
Traits of a good PM: people person, ability to lead, to delegate, to have integrity, excellent communicator, enthusiastic, empathetic, remain calm under pressure and some domain knowledge.
This wraps up our PMTuesdays series for this week.

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