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.
- Please use the
-recursive
flag when cloning:git clone --recursive https://github.com/khanhptnk/iliad.git
- Download and extract data:
cd data && bash download_data.sh
(3.1GB)
-
cd code
-
Build Docker image:
bash scripts/build_docker.sh
(usesudo
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
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 eitherNAV
orREGEX
. -
Train a baseline as:
bash scripts/train_$BASELINE.sh
where$BASELINE
is one ofdagger
,reinforce_binary
,reinforce_continuous
. -
Train an ILIAD/ADEL agent:
- Train the teacher's execution policy:
bash scripts/train_executor.sh
- Train the teacher's describer:
bash scripts/train_describer.sh
REGEX
only! initialize the student with unlabeled executions:bash scripts/pretrain_iliad.sh
- Train the student's with ILIAD/ADEL:
bash scripts/train_iliad.sh
- Train the teacher's execution policy:
-
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 theconfigs
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 ofiliad
,dagger
,reinforce_binary
,reinforce_continuous
.
@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}
}
If you have questions, please contact Khanh at kxnguyen@umd.edu or nguyenxuankhanhm@gmail.com.