Play So Hot Slot Machine Online – Reinforcement learning greatly improves many applications, especially for monitoring customer behavioral expectations in e-commerce, including where I work as a data scientist, Wayfair. Another popular way of framing RL algorithm problems is as a “thief with many arms”, but I always think that this term is not necessarily difficult, given that it should be a useful example. First, it is a 100-year-old “one-armed bandit”, and secondly, the image of a machine with many weapons is beautiful to draw.
Modern slot machines may have different buttons to press, which at least appear to produce different variations, but the best similarity would be that most machines in casinos, some “mess” – Sacco “and others” m. When I walked into the game corner of Celadon City in the 2004 Game Boy Advance game Pokemon Fire Red, and saw rows of slot machines with all different possibilities, I knew I had found the perfect “real life” version of this example – And useful. Use strength training.
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Celadon’s Game Corner: A pit of evil, destruction, and loss of life. (Screenshot by author suitable for use for teaching, professional and research purposes)
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And I mean brave! How else am I going to spend 4000 coins to buy an Ice Beam or Flame Thrower power-up, which I’ll need to fight the Elite Four?
I made a motivated agent, using Thompson’s model, tell me which machine to try, and eventually, which one to play with. I call it MACHAMP: a handheld multi-armed amplifier made for Pokemon.
Given a list of possible actions (many gunners’ “weapons”—in this case, various test tools), Thompson’s sampler researches abuse to find the best action. , by testing the promise function multiple times, so. Get a more detailed assessment of their potential payouts. At the same time, they still recommend others from time to time, if it turns out that one of them is better. At each step, the knowledge of the system, in the form of posterior probability distributions, is updated using Bayesian inference. The simplest version of the one-armed robber problem involves a Bernoulli trial, where there are only two possible outcomes, paying or not paying, and we try to determine which action has the highest probability of paying.
As an example of how Thompson’s model works, imagine we have 4 slot machines, with payout probabilities of 20%, 30%, 50% and 45%. We can explain how the solver finds that slot 3 is the best. Here and in another notebook, I started from the code written by Lillian Wing for her excellent tutorial (everything
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Initially, we do not know anything about the probability of the instrument, and we assume that all values for their true payout probability are equal, from 0% to 100% (depending on the problem, this initial Bayesian choice (may be a bad assumption, as I discuss below).
One step of the solver consists of sampling randomly from the probability distribution of each previous device, trying the best (this is Thompson’s sampling algorithm), and then updating this distribution based on whether a reward is available.
We can see from the probability graph that one win for machine 4 makes us more confident about this machine – now we think there is a higher chance of predicting the possible prize.
After running 100 simulated loads on four machines, we can see that it is optimized for good value.
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And after 10000 tests we are sure that 3 are the most useful, because we sampled 3 more than others. We drew 4 more to confirm, but 1 and 2 we quickly realized were the worst and therefore were taken too low – we got lower and lower estimates of their potential payouts, but we didn’t care. had
There are 19 gaming machines in the Celadon gaming corner, which pay out with coins that can be used to buy TMs (potential Pokemon) and Pokemon that can’t be found anywhere else. Three wheels move, and you press buttons to make them stop one by one, the goal is to line up three identical pictures, or at least one combination that starts with a cherry.
It gives 6 coins, or “just enough to sustain its users” (author image useful for teaching, professional and research purposes)
The best jackpot is three 7s, for 300 coins, how do I know the machines have different odds? Because the game character told me so.
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Before jumping into something as complicated as Thompson’s MAB pattern solver, I searched online for more lottery tips. Perhaps because this is such an old game (I’ll get to them when I get to them) the information is sparse and sometimes contradictory:
So I decided to play the game by quickly hitting the “stop” button without paying attention to the visuals, just record whether a win (of any size) or a loss, and let the Thompson model through MACHAMP , guide me. Select the device for the next test.
To begin solving, though, I decided to test each device four times, and use the results to create a post probability. With only four models it is difficult to draw any conclusions about a good or bad machine, because the probability distribution is too wide – the time is not the same.
Since the overlap makes it difficult to read the statistics of individual machines, I instead set a confidence interval for each machine: a range of values with a certain probability, in this case 80%. It’s easy to pick which machine is more likely to be 4/4 or 0/4, and how unlikely a 0/4 machine is better than a 4/4 machine. However, a large amount of uncertainty remains, and there is clearly not enough room to choose the best tool.
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I then started using Solver to suggest the next slot machine. It’s great to feel the balance of research and use because the number sent me from one tool to another, with or without a reward. After each attempt I update MACHAMP with that reward (0 or 1), then ask for advice for the next attempt.
My way through the casino would look silly, like Billy walking the streets in the old Family Circus cartoons. Of course I fight the urge to stick with this tool that seems to be successful without other people, and instead I leave the “hot” to try something I haven’t thought of in years. People don’t think of Thompson products in the best ways!
I stopped after 1000 and pulled the levers, and looked at what I learned. First, there is a balance in the engines that I draw, and it comes down to the engines that are most efficient in terms of success.
The sample size is expressed in the final confidence intervals, which are generally wider for engines that are assumed to be bad.
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), but I can see which ones are better, and how they differ from those that are probably worse, e.g. 5 machines, which returned exactly 0 prizes in 8 drawings.
If I care about good readability on each machine, I can spread all 1000 drawings over 19 machines, 52 drawings each, but that will result in a lot of wasted cash when I have to activate the existing machines. continued. Obviously useless, what a pity it is called. Although to save time I didn’t track my winnings, or even count jackpots, after 1000 withdrawals, MACHAMP increased my bank from 120 to 3977 coins.
. It has one of the best payment probability statistics (42.1%), but, also important, it has a short safety period, thanks to all the times I tested it (119): I am sure that it is their is in the middle. the best
I made another 1000 reds on 9 machines, to test this calculation in practice, and produce this coin. (Also, it’s election day, and it’s better to start the news again…) In 1119 draws I won 37.7% of the time, which is lower than the MACHAMP value – although the 80% confidence limit in I think the algorithm is interesting to expect 50%, because of the results of previous images (starting with the expectation that all values between 0 and 100% are equal). Knowing what I know now, these machines do not pay more than 40%, I can start with a different type that allows me to get a better estimate with the same number of tests.
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Now that I’ve used 9 machines, I’ve also started tracking my properties for a while
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