Project Bonsai

Project Bonsai is a Machine Teaching service that abstracts the complexity of AI and enables non-software Engineers to create or equip industrial control systems with intelligence and autonomous decision-making capabilities. The service provides an IDE for Engineers where they can use simulations of the physical system for generating synthetic training data and author teaching curricula using a domain specific Machine Teaching language (called Inkling). The service uses advanced deep reinforcement learning algorithms under the hood and enables domain experts to harness their knowledge using constructs they are familiar with. The resulting systems can operate in dynamic physical environments, optimizing their operation through smart, autonomous decisions and actions.


If you have technical questions or need help with Project Bonsai, start by going here.
  1. Add Action Masking

    I would like to open it to discussion that having action masking capability in the platform. Action Masking is basically to define available actions (or suppress the forbidden ones, to be precise) to the agent based on the state the agent is in. For example, while playing a chess, you can move to bishop to the up-left diagonal, unless there is another piece there or it is outside of the board. When it is one of the latter, that move should be removed from the available actions. In most scenarios, the available action set is a function of state, usually…

    4 votes
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  2. Support Images for Input

    Today, Bonsai brains can take numeric input. It would be great if I could send images or video to the brain and it could learn to pick optimal actions given graphical state.

    3 votes
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  3. Importing a brain

    Instead of starting from scratch, I'd like to train a brain that has already been partially trained. That way I can reuse some existing work and build up on it.

    3 votes
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  4. Live guide for new users

    It would be helpful if there was a "wizard" that walked me through all parts of the user interface that I needed when I first launched my workspace. An interactive walkthrough that I can click through, as an example.

    3 votes
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  5. How to get from a training brain to the corresponding simulator?

    In the simulators view, I see that a sim is being used to train a brain (in the connections view). However, from the brain view, I don't see any way to get back to the sim.

    3 votes
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  6. Delete versions for a brain

    I would like to be able to delete versions for a brain in my workspace. I have many of them and it would be much easier to clean up the versions if I could delete some of them.

    3 votes
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  7. Export Brain in ONNX or Similar Format

    Include the ability to export a Brain in an ONNX https://onnx.ai/ format in addition to the ones currently listed

    3 votes
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  8. OpenAI Gym Environment Integrated with the Bonsai Platform

    Sample codes or guidances to connect to OpenAI Gym Environment with the Bonsai Platform would be great.

    2 votes
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  9. Add folders to help organize BRAINS and experiments

    It would be helpful to be able to group BRAINs into folders to help organize experiments, for example: Extrusion, Process1, Process2, etc.

    2 votes
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  10. Search list of brains

    I have a lot of brains in my workspace and it's hard to find the right ones. Can we add a search function so I can search for the right one?

    2 votes
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  11. Allow for #include in Inkling files

    Regarding Inkling syntax, does anybody else think that segregating various lessons, concepts, etc. into separate files would be a good thing? Maybe not for small projects, but as folks get more accustomed to doing incremental learning, grouping the learning stages might be useful. At some point, #include functionality might be a useful addition. Imagine #include "simpleconcepts.ink" or #include "basicschemas.ink"

    1 vote
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  12. Connect Azure Machine Learning workspace for model manegement or so.

    Model export to ACR is great.
    But to consider entire MLOps world with edge devices, it looks like connect Bonsai workspace and AML workspace is great.

    1 vote
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  13. Connect to GitHub

    To manage Inkling code, connecting GitHub or other sorce code management services is important.

    1 vote
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  14. As a user, I would like to see reported curriculum threshold status live

    So far the user sees this:
    "6/18 9:35 AM: Training will stop early if the curriculum has completed and a success rate of 0.9 has been achieved over 150 test episodes."

    Experimenting with machine learning often leads to making additional curriculum experiments based upon progress of training. I would find the following very helpful:

    current success rate = 0.87 over 150 test episodes.

    1 vote
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  15. Support for Q#

    Consider supporting Azure Quantum via Q#, which is a very natural extension for Bonsaii machine learning.

    1 vote
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  16. Add the ability to include expert policies (demonstration learning) in training.

    Expert Policies (Demonstration Learning) is a useful tool in solving difficult problems with off-policy reinforcement learning algorithms; it is a valid machine teaching strategy. It effectively generates enough good state transitions to help "explore" effective areas of learning. It will also probably be useful to be able to toggle what % of the time the expert policy gets used vs. exploring vs. exploiting. It will also likely need to slowly decay the use of the expert policy.

    1 vote
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  17. "Diff" View for versions

    A toggle button that would enable a diff against the previous version of the brain. It woudl utilize the same functionality that exists in VSCode, hopefully, showing a side-by-side full diff..

    1 vote
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  18. See multiple performance charts at once

    I find myself going back and forth between success rate, goal satisfaction, and the default charts all the time. I want to see the relevant ones all at once.

    1 vote
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