All arguments are preset in main.py, so you can start training the FRL model by:
$ python main.pyIf you want a more robust result, try the following command, which will train and test the FRL model for
$ bash both_train.shFor training the baseline model, DQN-alpha, run the following command:
$ bash alpha_train.shFor DQN-full, run:
$ bash full_train.shThere are three sub-domains in Text2Action: win2k, wikihow and cooking. For each sub-domain, you need to train both Action-Name Extractor and Action-Argument Extractor.
All parameters are preset in main.py, but you need to change some of them according to the sub-domain and the type of extractor.
Train FRL model
Take win2k sub-domain as an example.
- To train and test the Action-Name Extractor, run:
$ python main.py --domain 'win2k' --agent_mode 'act' --predict_net 'both' --train_mode 'frl_separate' --result_dir 'test_frl_act'- where
domainindicates the name of sub-domains,agent_modeindicates the type of extractor,predict_netandtrain_modeindicate the model name. - For Action-Argument Extractor, change the
agent_modetoargand run:
$ python main.py --domain 'win2k' --agent_mode 'arg' --predict_net 'both' --train_mode 'frl_separate' --result_dir 'test_frl_arg'You can easily change the parameter --domain to 'cooking' or 'wikihow' and repeat the procedure to train models for the other two sub-domains.
Train DQN-alpha model
Take win2k sub-domain as an example.
- To train and test the Action-Name Extractor, run:
$ python main.py --domain 'win2k' --agent_mode 'act' --predict_net 'alpha' --train_mode 'single_alpha' --result_dir 'test_dqn_alpha_act'- For Action-Argument Extractor, change the
agent_modetoargand run:
$ python main.py --domain 'win2k' --agent_mode 'arg' --predict_net 'alpha' --train_mode 'single_alpha' --result_dir 'test_dqn_alpha_arg'You can easily change the parameter --domain to 'cooking' or 'wikihow' and repeat the procedure to train models for the other two sub-domains.
Train DQN-full model
Take win2k sub-domain as an example.
- To train and test the Action-Name Extractor, run:
$ python main.py --domain 'win2k' --agent_mode 'act' --predict_net 'full' --train_mode 'full' --result_dir 'test_dqn_full_act'- For Action-Argument Extractor, change the
agent_modetoargand run:
$ python main.py --domain 'win2k' --agent_mode 'arg' --predict_net 'full' --train_mode 'full' --result_dir 'test_dqn_full_arg'You can easily change the parameter --domain to 'cooking' or 'wikihow' and repeat the procedure to train models for the other two sub-domains.