Yuhang Song, Anh Nguyen, Chun-Yi Lee
PyTorch implementation of our ACML 2023 paper Learning to Terminate in Object Navigation in AI2-THOR environment. This implementation is modified based on SAVN and MJOLNIR_O. Please refer to our paper for more details.
The offline data can be found here.
"data.zip" (~5 GB) contains everything needed for evalution. Please unzip it and put it into the project folder.
For training, please also download "train.zip" (~9 GB), and put all "Floorplan" folders into ./data/thor_v1_offline_data
Note that DITA needs to specify a different agent_type in both training and testing.
python main.py --eval \
--test_or_val test \
--episode_type TestValEpisode \
--load_model pretrained_models/DITA.dat \
--model DITA \
--results_json dita.json \
--gpu-ids 0 \
--load_JG_model pretrained_models/JudgeModel.dat \
--agent_type SupervisedNavigationAgent
Evaluating the DITA model result in auto-generations of action log files for visulization.
If you have trained other models ("SAVN" or "GCN" or "MJOLNIR_O"), please evaluate them using the following command.
python main.py --eval \
--test_or_val test \
--episode_type TestValEpisode \
--load_model [model_name] \
--model MJOLNIR_O \
--results_json mjolnir_o.json \
--gpu-ids 0 \
--agent_type NavigationAgent
--judge_model False
Other model options are "SAVN" or "GCN" or "MJOLNIR_O".
Note that our visualization only supports DITA model.
cd visualization
python visualization.py --actionList ../dita_vis.log
Note that DITA needs to specify a different agent_type in both training and testing.
python main.py \
--title mjolnir_train \
--model MJOLNIR_O \
--gpu-ids 0\
--workers 8
--vis False
--save-model-dir trained_models
--agent_type NavigationAgent
Other model options are "SAVN" or "GCN" or "MJOLNIR_O".
python main.py \
--title DITA_training \
--model DITA \
--gpu-ids 0\
--workers 8
--vis False
--save-model-dir trained_models
--agent_type SupervisedNavigationAgent