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InchwormRL

By Schuyler Fenton, Grant Holland, Nathan McCutchen, and Ishan Meher.

This project is for CSC 480 at Cal Poly, taught by Professor Rodrigo Canaan.

External Resources

We used the following external resources in our project.

MuJoCo physics engine: https://mujoco.readthedocs.io/en/stable/overview.html
Gymnasium RL framework: https://gymnasium.farama.org/
Stable Baselines3 RL algorithms: https://stable-baselines3.readthedocs.io/en/master/index.html

Setup

Follow these instructions to set up the codebase locally.

1. Clone the Repo

Run your favorite version of the git clone command on this repo. One version:

git clone [email protected]:gwholland3/InchwormRL.git

2. Install Python

This code was developed and run on Python 3.10.10, but most likely any version of Python 3.10 will do. Make sure you have an appropriate version installed locally.

3. Install Requirements

We recommend doing this in a fresh Python virtual environment. Cd into the repo and run:

pip3 install -r requirements.txt

Interacting With the Project

Our whole project has a single entry point, run_environment.py. You can control the functionality via command-line arguments to Python script.

Examples

Run an existing trained model (inchworm3.0_td3) and print evaluation data:

python3 run_environment.py -rsem inchworm3.0_td3

Run an existing trained model (inchworm2.1_td3) on the old Inchworm environment and print evaluation data:

python3 run_environment.py -rsoem inchworm2.1_td3

Train a new model (inchworm3.1_td3) with 10,000,000 timesteps:

python3 run_environment.py -tm inchworm3.1_td3 -T 10000000

usage: run_environment.py [-h] (-t | -r | -R | -c) [-m MODEL_NAME] [-a ALGORITHM] [-s] [-e] [-o]
                          [-T TOTAL_TIMESTEPS] [-l LEARNING_RATE]

Run or train an agent to control an inchworm robot

options:
  -h, --help            show this help message and exit

Functional arguments (mutually exclusive):
  -t, --train           train a new/existing model in test_models/ with the TD3 algorithm
  -r, --run             run a model with the TD3 algorithm
  -R, --random          run the environment with random actions
  -c, --control         run the environment with user control

Training and running arguments:
  -m MODEL_NAME, --model-name MODEL_NAME
                        name of the model to run (minus the .zip extension)
  -a ALGORITHM, --algorithm ALGORITHM
                        algorithm to use for training/running model, either sac or td3 (default: td3)

Running arguments:
  -s, --saved-dir       whether the model will be/is in the saved_models/ directory (otherwise test_models/)
  -e, --eval            whether to print out evaluation data while running the simulation
  -o, --old-model       whether the model was trained with the old version of the Inchworm environment

Training arguments:
  -T TOTAL_TIMESTEPS, --total-timesteps TOTAL_TIMESTEPS
                        total number of timesteps to train the model for (default: 1,000,000)
  -l LEARNING_RATE, --learning-rate LEARNING_RATE
                        learning rate for training the model (default: 0.0003)