/softagent

Algorithms for deformable object manipulation benchmarked in SoftGym

Primary LanguagePython

SoftAgent

This repository contains the benchmarked algorithms for environments in SoftGym (paper). The benchmarked algorithms include

Installation

  1. Install SoftGym by following the instructions in SoftGym repository. Then, copy the softgym code to the SoftAgent root directory so we have the following file structure:

    softagent
├── cem
├── ...
├── softgym

  2. Update conda env with additional packages required by SoftAgent: conda env update --file environment.yml --prune

  3. Activate the conda environment by running . ./prepare_1.0.sh.

  4. For running MVP, please refer to the original implementation for dependencies.

Running benchmarked experiments

  1. Generating initial states for different SoftGym environments: python experiments/generate_cached_states.py

  2. Running CEM experiments: python experiments/run_cem.py. Refer to run_cem.py for different arguments.

  3. Running CURL/SAC experiments: python experiments/run_curl.py. Refer to run_curl.py for different arguments.

  4. Running PlaNet experiments: python experiments/run_planet.py. Refer to run_planet.py for different arguments.

  5. Running DrQ experiments: python experiments/run_drq.py. Refer to run_drq.py for different arguments.

  6. Train an MVP policy: python experiments/run_mvp.py. Refer to run_mvp.py for different arguments. Once the model is trained, use rlpyt_cloth/max_q_eval_policy to evaluate the policy that selects the pick location with the maximum Q value.

Note: Default number of environment variations are set to 1. Set them to 1000 to reproduce the original experiments.

Cite

If you find this codebase useful in your research, please consider citing:

@inproceedings{corl2020softgym,
 title={SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation},
 author={Lin, Xingyu and Wang, Yufei and Olkin, Jake and Held, David},
 booktitle={Conference on Robot Learning},
 year={2020}
}

References