Skip to content

SEER - Stabilizing Energy Efficient Controller Manipulators using Reinforcement Learning

Notifications You must be signed in to change notification settings

Paralelopipet/SEER

Repository files navigation

SEER

SEER - Combining Analytic Control with Learning to Create a Stabilizing Controller that Works in Reality

Built by Damjan Denic, Martin Graf, Nav Leelarathna, and Sepand Dyanatkar

Setup

  1. Clone the repository inclusive submodules git clone --recurse-submodules [email protected]:Paralelopipet/SEER.git
  2. Pretrained weights are available here.
    • If you want to use them, place them in the folder pybullet_multigoal_implementation/drl_implementation/examples
  3. To setup natively:
    1. Create conda environment conda env create -f environment.yml
      • note: this will take a while (3 to 4 coffees)
    2. Activate environment conda activate l32_seer
    3. Install our gym package pip install --editable pybullet_multigoal_gym
    4. Install our RL package pip install --editable pybullet_multigoal_implementation
    5. Install seer package pip install --editable .
  4. To setup with Docker:
    1. Run docker build -f evaluate.Dockerfile -t seer-evaluate .
      • this needs to be repeated whenever any files were changed

Test (requires native install)

Run pytest

Train

  • to train natively, run python seer/train_and_eval_configs/config_runner.py --config seer.train_and_eval_configs.rl_training.<scenario to train>
  • To train using Docker, run docker run --memory=6g --cpus=4 --mount "type=bind,source=$PWD/pybullet_multigoal_implementation/drl_implementation/examples,target=/root/pybullet_multigoal_implementation/drl_implementation/examples" -it seer-evaluate --config seer.train_and_eval_configs.rl_training.<scenario to train>
    • in CMD, replace $PWD by the absolute path of this directory
  • In both cases, replace <scenario to train> with the name of the Python file containing the scenario you want to train (i.e., rl_config_train_basic)
  • weights are saved to the pybullet_multigoal_implementation/drl_implementation/examples folder

Evaluate

  • to evaluate natively, run python seer/train_and_eval_configs/config_runner.py --config seer.train_and_eval_configs.<scenario to evaluate>
  • To train using Docker, run docker run --memory=4g --cpus=2 --mount "type=bind,source=$PWD/pybullet_multigoal_implementation/drl_implementation/examples,target=/root/pybullet_multigoal_implementation/drl_implementation/examples" -it seer-evaluate --config seer.train_and_eval_configs.<scenario to evaluate>
    • in CMD, replace $PWD by the absolute path of this directory
  • In both cases, replace <scenario to evaluate> with the partial package name of the Python file containing the scenario you want to evaluate (i.e., rl_eval.basic.rl_config_eval_basic or baseline.baseline_config_noisy_slope)
  • if evaluating the reinforcement learning solution, weights need to be present in the pybullet_multigoal_implementation/drl_implementation/examples folder

Debug in VSCode

  • press F5 (launch configuration RL Trainer)
  • or try out the other launch configurations in .vscode/launch.json

About

SEER - Stabilizing Energy Efficient Controller Manipulators using Reinforcement Learning

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Contributors 4

  •  
  •  
  •  
  •