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Interactive Learning from Activity Description (ILIAD)

PythonPyTorch

Implementation of experiments in Interactive Learning from Activity Description (ICML 2021).

ILIAD is an interactive learning framework that enables training agents using only language description feedback.

Clone Repo

  • Please use the -recursive flag when cloning: git clone --recursive https://github.com/khanhptnk/iliad.git

Download Data

  • Download and extract data: cd data && bash download_data.sh (3.1GB)

Setup Docker

  • Install Docker and Nvidia Container Toolkit

  • cd code

  • Build Docker image: bash scripts/build_docker.sh (use sudo if needed)

  • Run Docker image: bash scripts/run_docker.sh. If you successfully launch the image, the terminal prompt will end with # instead of $.

  • Inside the image, build the Matterport3D simulator:

# cd iliad/code
# bash scripts/build_simulator.sh

and create experiments directories:

# mkdir tasks/NAV/experiments
# mkdir tasks/REGEX/experiments

Run Experiments

All commands in this section must be run inside the Docker image! (where the prompt starts with #)

  • Go to the NAV directory: cd iliad/code/tasks/$TASK where $TASK is either NAV or REGEX.

  • Train a baseline as: bash scripts/train_$BASELINE.sh where $BASELINE is one of dagger, reinforce_binary, reinforce_continuous.

  • Train an ILIAD/ADEL agent:

    1. Train the teacher's execution policy: bash scripts/train_executor.sh
    2. Train the teacher's describer: bash scripts/train_describer.sh
    3. REGEX only! initialize the student with unlabeled executions: bash scripts/pretrain_iliad.sh
    4. Train the student's with ILIAD/ADEL: bash scripts/train_iliad.sh
  • For each experiment, a log file will be saved to experiments/$NAME/run.log where $NAME is the name of the experiment specified in the YAML config file of the experiment (these config files are in the configs folder; you can view an experiment's .sh script to see what config file it is using).

  • Evaluate an agent: bash scripts/eval.sh $METHOD where $METHOD is one of iliad, dagger, reinforce_binary, reinforce_continuous.

Citation

@inproceedings{nguyen2021iliad,
  title={Interactive Learning from Activity Description},
  author={Nguyen, Khanh and Misra, Dipendra and Schapire, Robert and Dud{\'\i}k, Miro and Shafto, Patrick},
  booktitle={Proceedings of the 38th International Conference on Machine Learning},
  year={2021},
  url={https://arxiv.org/pdf/2102.07024.pdf}
}

Contact

If you have questions, please contact Khanh at kxnguyen@umd.edu or nguyenxuankhanhm@gmail.com.

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