Reinforcement learning using the technique of TD-Gammon, for highly qualified migrations localizations in Google-Bing-Duckduckgo-Citeseerx
The script is called training_script.py
which has the following arguments:
- "DB", "Path to training directory"
- "ALG", "Algorithm to execute", default="DQN"
- "is_RE", "Use of Regular Expression", default="0"
- "-is_test", "The data is for testing", required=False, default=0
- "-initial_range", "Initial range of users", required=False
- "-final_range", help="Final range of users", required=False
- "-is_db_v2", help="Is the second database", required=False
python3 training_script.py ~/project/dospordos/DATA/db_v1_ns/train_db/ DQN 0
This means that you'll be using:
- the train database (data source) with path ~/project/dospordos/DATA/db_v1_ns/train_db/
- DQN model instead of a DDQN
- 0 is for using Regex or NE and is_test is for using a train data source or test data source.
python3 training_script.py ~/project/dospordos/DATA/db_v2_ns/test_db/ DQN 0 -is_test=1 -is_db_v2=1
This means that you'll be using:
- the test database db_v2_ns (data source)
- is the second database
Depending on the parameters given the data will be stored as DQN_0_db_v1_ns* in the DATA directory
There are other optional parameters to run a specific range of users which are -initial_range and -final_range
python training_script.py /users/urbinagonzalez/project/dospordos/DATA/db_v1_ns/test_db/ DQN 0 -is_test=1 -final_range=45
- This will run the users up to the user 45. Is doing list_users[:45]
The data directory should have folders with numbers
- ~/DATA/train
- 3
- 5
- ...
- 4904
- 4905
- ...
Besides running the build.sh
- python -m spacy download en
- Install keras, tensorflow
####Notes
In the class DQN of DQN_implementation.py you can set the callbacks used for stopping the network.
self.callbacks = [agent.EarlyStopByLossVal(value=0.1), agent.EarlyStopping(patience=10)]
##For testing Use TESTS/evaluate_test_run script, you should already have all the pkl files you want to average and graph. This is the format you should follow:
python3 evaluate_test_run.py -r DQN_0_db_v1_ns_rm.pkl -acc DQN_0_db_v1_ns_acc.pkl -g 1
For more details:
python3 evaluate_test_run.py -h
ssh -p 60022 urbinagonzalez@tal.lipn.univ-paris13.fr
in GPU2 The directory for the project is
~/project/dospordos
The virtual environment is called venv-dospordos
There's a tmux session ready. To connect
tmux a -t base