The purpose of PolicyPrep is to automate the process of running experiments with deep learning frameworks like PyTorch (although TensorFlow and others are possible). The pipeline is designed to be run on a remote machine, such as a server, and will run continuously until stopped. Specifically, this pipeline was built to accommodate offline reinforcement learning of hierarchical policies where the reward information has been delayed. PolicyPrep will collect data from multiple sources, aggregate the data, infer immediate reward information, and train a policy using reinforcement learning (via d3rlpy) to be used in your next experiment. The pipeline will also generate a report of the results of the experiments and the policy that was trained. I developed PolicyPrep during my Ph.D. to save time and resources for myself and my colleagues where we used it to train policies for e-learning apps called Intelligent Tutoring Systems. As such, some code may still contain references to this educational domain, but it can be easily modified for your own application.

The PolicyPrep is not only for use with RL policy induction. That is what it's initially created for, but with a few minor tweaks, it can facilitate any experiment-related study, such as Inverse RL (this would be a matter of changing step 9 at this time of writing) or even posthoc analysis. It is meant to be a consistent and uniform platform for all members involved in your experiment's projects - at least, in the coming future, it will be.

Essentially, rather than each of us re-invent the wheel by collecting study data, aggregating it together, etc., the pipeline, at the very least, can perform these operations for you. This would involve running a smaller subset of the total steps in the pipeline or creating your own custom steps and appending those to it (e.g., make it offshoot into doing RL and Inverse RL simultaneously). The whole purpose of this effort is to save your time, so you can focus on other things, such as preparing your research question or having extra free time (yes, glorious free time 🤯). In the past, I have spent weeks or even months preparing my own local experiment setup, so your savings concerning time are significant. For a new Ph.D. student, a conservative estimate on time saved (as things are right now) amounts to at least 2 months throughout your entire Ph.D., as we have eliminated the need for looking up the data, costly edits, manually updating training data, or writing "fixes" to patch InferNet, as well as formatting the data automatically for you to use with RL (or Inverse RL, as previously stated).

It also serves as additional documentation on how we perform experiment setup for policies, guiding new students from start to finish on what we expect explicitly, so they can confidently continue their study knowing they have completed the necessary steps correctly.

Furthermore, by accepting the PolicyPrep in your workflow, it would be easier to assist you if you face trouble implementing your policy, as opposed to "you're on your own" if your code doesn't work. I hope establishing PolicyPrep will allow us to collaborate more closely and pursue research endeavors we otherwise might not have had the time or resources in the past.

Lastly, the pipeline is meant to be a living project, meaning it will be updated and improved over time. Under all circumstances, do not hesitate to reach out to me if you have any questions or concerns. I am more than happy to help you with any issues you may have, and I am open to any suggestions you may have to improve the pipeline. I am also open to any contributions you may have to the project as I am sure there are many ways to improve the code. However, I am confident that the pipeline will be a useful tool for all of us.

Please do not let the pipeline intimidate you or be a barrier to your research. It is meant to be a tool to help you, not hinder you. Although it was originally designed to be used for Artificial Intelligence in Education, it can be modified for your intended application if it meets the above specified problem criteria (i.e., hierarchical tasks with delayed rewards). I am here to help you with any issues you may have, and I am more than happy to do so.

If you are interested in working together, please feel free to reach out to me regarding any questions you may have about incorporating the pipeline into your work ([email protected]). I would also appreciate any help offered to ensure the robustness of the project.

Sincerely,

John Wesley Hostetter

The following are required to run the pipeline:

• For packages, see requirements.txt
• Python 3.8.5

This project is written in Python 3.8.5. It is recommended to use a virtual environment to run this project. The following instructions are for setting up the project on a Linux or macOS machine. The instructions may vary slightly for other operating systems.

Within PyCharm's markdown preview, the following bash commands can be executed by clicking on the play button that appears when hovering over the command.

1. Clone the repository:

   git clone https://github.com/johnHostetter/PolicyPrep.git

2. Change directory into the PolicyPrep directory:

   cd PolicyPrep

3. Create a virtual environment (within the PolicyPrep directory):

   python3 -m venv venv

4. Activate the virtual environment:

   source venv/bin/activate

5. Install the required packages:

   pip install -r requirements.txt

6. Pip install the project into an editable state, so that changes to the code will be reflected in the environment:

   pip install -e .

7. Verify step 6 executed correctly by running the following command(s):

   1. pip list

      The output should contain the following line:

      PolicyPrep            1.0        /home/johnhostetter/PycharmProjects/PolicyPrep (or wherever the project is located)

   2. pip show PolicyPrep

      The output should be:

      Name: PolicyPrep
      Version: 1.0 (or whatever version is currently installed)
      Summary: UNKNOWN
      Home-page: UNKNOWN
      Author: John Wesley Hostetter
      Author-email: [email protected]
      License: UNKNOWN
      Location: /Users/jwhostet/PolicyPrep (or wherever the project is located)
      Requires:
      Required-by:

   3. pip freeze

      The output should contain the following line:

      -e git+https://github.com/johnHostetter/PolicyPrep@5856f3b709750d5a6aa7403f8c0c5668873fbd5f#egg=PolicyPrep

8. Run the pipeline (two ways):

   1. In the foreground (good for testing):

      python3 src/pipeline.py

   2. In the background (good for running the pipeline for a long time):

      nohup python3 -u src/pipeline.py &

      After running this command, you may need to hit ENTER on your keyboard. The process is now being executed in the background, meaning you can perform other commands in the terminal, or close the terminal (e.g., ssh disconnect). The output to the terminal will be written to a file called nohup.out in the PolicyPrep directory. View the contents with the following command:

      vi nohup.out

      Move to the end of the file with :$ and type :q to exit the file. Be sure to view this file is being updated periodically to ensure the pipeline is running correctly. I recommend every few hours (before training of InferNet) to ensure the pipeline is running. Once you reach the training of InferNet, I recommend checking the file once every day or two.
   3. To stop the pipeline (if running in the foreground), press CTRL + C.
   4. To stop the pipeline (if running in the background), run the following command:

      ps aux | grep pipeline.py

      The output will be similar to the following:

      jwhostet  12345  0.0  0.0  12345  1234 pts/0    S+   00:00   0:00 python3 src/pipeline.py
      jwhostet  12346  0.0  0.0  12345  1234 pts/0    S+   00:00   0:00 grep --color=auto pipeline.py

      The first number in the second column is the process ID (PID) of the pipeline. To stop the pipeline, run the following command:

      kill -9 12345

      where 12345 is the PID of the pipeline. You can verify the pipeline has stopped by running the following command:

      ps aux | grep pipeline.py

      An alternative to the above command to find the process ID is to run the following command:

      ps -ef | grep pipeline.py

9. The output will be generated in the data folder, underneath the subdirectory called for_policy_induction. Within this subdirectory, there will be two folders: pandas and d3rlpy, containing .csv files or .h5 files for policy induction via reinforcement learning, respectively.

1. Initially when the pipeline.py is being run on the macOS (with a M1 Processor), a problem is being encountered while building up the wheel for lxml:

   Building wheel for lxml (setup.py) ... error

   error: subprocess-exited-with-error

   ERROR: Failed building wheel for lxml

   Looking more into the issue, Adittya Soukarjya Saha came across this:

   Pip install on macOS gets error: command '/usr/bin/clang' failed with exit code 1

   FIX: Updating the "setup tools":

   python3 -m pip install --upgrade setuptools

2. List of all the severe warnings(14) that were generated while running the code are given below. Some of the requirements from the list provided in the requirements.txt file can be ignored as the code ran without using any of those listed, just fine.

• 'nvidia-cublas-cu11 11.10.3.66' is not installed (required: 11.10.3.66, installed: <nothing>, latest: 11.11.3.6)

• 'nvidia-cuda-cupti-cu11 11.7.101' is not installed (required: 11.7.101, installed: <nothing>, latest: 11.8.87)

• 'nvidia-cuda-nvrtc-cu11 11.7.99' is not installed (required: 11.7.99, installed: <nothing>, latest: 11.8.89)

• 'nvidia-cuda-runtime-cu11 11.7.99' is not installed (required: 11.7.99, installed: <nothing>, latest: 11.8.89)

• 'nvidia-cudnn-cu11 8.5.0.96' is not installed (required: 8.5.0.96, installed: <nothing>, latest: 8.9.2.26)

• 'nvidia-cufft-cu11 10.9.0.58' is not installed (required: 10.9.0.58, installed: <nothing>, latest: 10.9.0.58)

• 'nvidia-curand-cu11 10.2.10.91' is not installed (required: 10.2.10.91, installed: <nothing>, latest: 10.3.0.86)

• 'nvidia-cusolver-cu11 11.4.0.1' is not installed (required: 11.4.0.1, installed: <nothing>, latest: 11.4.1.48)

•  'nvidia-cusparse-cu11 11.7.4.91' is not installed (required: 11.7.4.91, installed: <nothing>, latest: 11.7.5.86)

•  'nvidia-nccl-cu11 2.14.3' is not installed (required: 2.14.3, installed: <nothing>, latest: 2.18.3)

•  'nvidia-nvtx-cu11 11.7.91' is not installed (required: 11.7.91, installed: <nothing>, latest: 11.8.86)

•  'tensorflow-io-gcs-filesystem 0.32.0' is not installed (required: 0.32.0, installed: <nothing>, latest: 0.32.0)

•  'triton 2.0.0' is not installed (required: 2.0.0, installed: <nothing>, latest: 2.0.0.post1)

•  'tensorflow 2.12.0' is not installed (required: 2.12.0, installed: 2.13.0, latest: 2.13.0)

The requirements that can be ignored are given below:

1. nvidia-cublas-cu11==11.10.3.66
2. nvidia-cuda-cupti-cu11==11.7.101
3. nvidia-cuda-nvrtc-cu11==11.7.99
4. nvidia-cuda-runtime-cu11==11.7.99
5. nvidia-cudnn-cu11==8.5.0.96
6. nvidia-cufft-cu11==10.9.0.58
7. nvidia-curand-cu11==10.2.10.91
8. nvidia-cusolver-cu11==11.4.0.1
9. nvidia-cusparse-cu11==11.7.4.91
10. nvidia-nccl-cu11==2.14.3
11. nvidia-nvtx-cu11==11.7.91
12. tensorflow-io-gcs-filesystem==0.32.0
13. triton==2.0.0

There were some more weak warnings, but those can be ignored.