A reinforcement learning approach for petri nets regarding human-robot collaboration tasks. Still a WIP.
You'll need to download and set up Stable Baselines3 - Contrib, which implements the MaskablePPO implementation utilized in this repo. All Python requirements are outlined in requirements.txt, meaning you can run pip install -r requirements.txt to install all needed Python packages.
If you want to view the training logs, you can also set up a docker image for Tensorboard.
Download this docker image which is a fully configured environment (minus tensorboard). If you want to view the training logs, you can also set up an additional docker image for Tensorboard.
After cloning the image, navigate to where this repo is installed and run: docker run --rm=true -it -v ${pwd}:/scratch -w /scratch <docker image> /bin/bash. This runs the docker image and connects a volume containing the current directory to it at /scratch. This will allow you to run the various python scripts in the environment.
To train a model, you can run python build_model.py with the following parameters:
--input-file <path_to_file: string>: Default is None. This file provides the metadata for the job.--base-model <path_to_file: string>: Default is None. The use of this parameters specifies a model to load and start the learning process from.--prepend <name: string>: Default is None. This is an additional string you can prepend to output file names.--process <id: int>: Default is 0. This is an integer appended to the output model filename for use in parallel setups.--iters <num: int>: Default is 50.--use-tensorboard: Default is False. Addition of flag is True. This flag adjusts adjusts whether to output log files.--chtc: Default is False. Addition of flag is True. This flag adjusts the execution for use on the center for high throughput computing.
At minimum, you need to specify an input file. These input files are job.json files produced by the Allocobot repo. Job files contain all information regarding the relative weighting of different metrics; the tasks, primitives, environmental targets, and agents involved in a job; and the Petri Net with all the costs associated for transitions. An example job.json file has been included in this repo, and represents a mock task.
You can run python evaluate_model.py to sample allocations and timelines from your learned model, with the following parameters:
--input-file <path_to_file: string>: Default is None. This file provides the metadata for the job.--model <path_to_file: string>: Default is None. The use of this parameters specifies a model to load and sample from.--output <filename: string>: Default is None. If None, the output filename will be the same as the input-file name with.jsonreplaced with-output.csv.--n-steps <num: int>: Default is 1000. This represents the maximum number of steps to run the model for. If the model reaches the goal state, it will terminate beforen_stepsis reached.--n-samples <num: int>: Default is 0. This represents the number of sample traces to compare from the model.
At minimum, an input file and model is required for the evaluation to work. This leaves the model in a stocastic mode, allowing you to sample multiple traces.
You can run python create_allocation_visual.py with the following parameters to understand some of the metrics from the saved trace from the model:
--input-file <path_to_file: string>: Default is None. This file provides the metadata for the job.--csv-file <path_to_file: string>: Default is None. This file represents the output from theevaluate_modelscript.
Running this script will analyze the allocation of tasks from the job between the various agents, and output metrics such as task time. Additional analysis will require investigating the CSV outputs manually.
If you want to combine this with the prior sampling, you can use the eval.sh script to run both commands. Eval works as follows: eval.sh <input-file> <n-steps> <n-samples> <model>, and will first run evaluate_model followed by create_allocation_visual.