BanditsBook | Code for my book on Multi-Armed Bandit Algorithms | Learning library

 by   johnmyleswhite R Version: Current License: Non-SPDX

kandi X-RAY | BanditsBook Summary

kandi X-RAY | BanditsBook Summary

BanditsBook is a R library typically used in Tutorial, Learning applications. BanditsBook has no bugs, it has no vulnerabilities and it has medium support. However BanditsBook has a Non-SPDX License. You can download it from GitHub.

Code for my book on Multi-Armed Bandit Algorithms
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              BanditsBook has a medium active ecosystem.
              It has 778 star(s) with 254 fork(s). There are 51 watchers for this library.
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              It had no major release in the last 6 months.
              There are 4 open issues and 3 have been closed. On average issues are closed in 0 days. There are 4 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of BanditsBook is current.

            kandi-Quality Quality

              BanditsBook has 0 bugs and 0 code smells.

            kandi-Security Security

              BanditsBook has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              BanditsBook code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              BanditsBook has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

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              BanditsBook releases are not available. You will need to build from source code and install.
              Installation instructions, examples and code snippets are available.

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            BanditsBook Key Features

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            BanditsBook Examples and Code Snippets

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            Community Discussions

            Trending Discussions on BanditsBook

            QUESTION

            Bandits with Rcpp
            Asked 2018-Apr-09 at 10:32

            This is a second attempt at correcting my earlier version that lives here. I am translating the epsilon-greedy algorithm for multiarmed bandits.

            A summary of the code is as follows. Basically, we have a set of arms, each of which pays out a reward with a pre-defined probability and our job is to show that by drawing at random from the arms while drawing the arm with the best reward intermittently eventually allows us to converge on to the best arm.

            The original algorithm can be found here.

            ...

            ANSWER

            Answered 2018-Apr-09 at 10:32

            In this piece of code:

            Source https://stackoverflow.com/questions/49727727

            QUESTION

            Multi-armed bandits with Rcpp
            Asked 2018-Apr-03 at 12:18

            I am translating the epsilon-greedy algorithm for multiarmed bandits from here. This is a rather nice demonstration of the power and elegance of Rcpp. However, the results from this version do not tally with the one that is mentioned in the link above. I am aware that this is probably a very niche question but have no other venue to post this on!

            A summary of the code is as follows. Basically, we have a set of arms, each of which pays out a reward with a pre-defined probability and our job is to show that by drawing at random from the arms while drawing the arm with the best reward intermittently eventually allows us to converge on to the best arm. A nice explanation of this algorithm is provided by John Myles White.

            Now, to the code:

            ...

            ANSWER

            Answered 2018-Apr-03 at 12:18

            I don't know how the bandits are supposed to work, but a little standard debugging (ie: look at the values generated) revealed that you generated lots of zeros.

            After fixing some elementary errors (make your C/C++ loops for (i=0; i ie start at zero and compare with less-than) we are left with other less subtle errors such as runif(1,N) cast to int not giving you a equal range over N values (hint: add 0.5 and round and cast, or sample one integer from the set of 1..N integers).

            But the main culprit seems to be your first argument epsilon. Simply setting that to 0.9 gets me a chart like the following where you still the issue with the last 'half' unit missing.

            Source https://stackoverflow.com/questions/49629277

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install BanditsBook

            To try out this code, you can go into the Python or Julia directories and then run the demo script.

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            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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