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FRL

Code for the paper 'Federated Reinforcement Learning'

Running

GridWrold

All arguments are preset in main.py, so you can start training the FRL model by:

$ python main.py

If you want a more robust result, try the following command, which will train and test the FRL model for $k$ times and save the average results.

$ bash both_train.sh

For training the baseline model, DQN-alpha, run the following command:

$ bash alpha_train.sh

For DQN-full, run:

$ bash full_train.sh

Text2Action (EASDRL)

There 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 domain indicates the name of sub-domains, agent_mode indicates the type of extractor, predict_net and train_mode indicate the model name.
  • For Action-Argument Extractor, change the agent_mode to arg and 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_mode to arg and 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_mode to arg and 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.