You will need to install Torch and the python packages in requirements.txt.
You will also need to install the Lua dev library liblua (sudo apt-get install liblua5.2) and the signal package for Torch to deal with SIGPIPE issues in Linux.
(You may need to uninstall the signal-fft package or rename it to avoid conflicts.)
Create the vectorizers (using a pre-trained model), for example:
python vec_consolidate.py dloads/Shooter/train.extra 5 trained_model2.p consolidated/vec_train.5.p
python vec_consolidate.py dloads/Shooter/dev.extra 5 trained_model2.p consolidated/vec_dev.5.p
Consolidate the articles, for example:
python consolidate.py dloads/Shooter/train.extra 5 trained_model2.p consolidated/train+context.5.p consolidated/vec_train.5.p
python consolidate.py dloads/Shooter/dev.extra 5 trained_model2.p consolidated/dev+context.5.p consolidated/vec_dev.5.p
Make sure you use the correct vec_xyz.p file as input to the consolidate.py script. You can also find pre-consolidated files in pickle format here: link
-
Change to the code directory:
cd code/ -
First run the server, for example:
python server.py --port 7000 --trainEntities consolidated/train+context.5.p --testEntities consolidated/dev+context.5.p --outFile outputs/run.out --modelFile trained_model2.p --entity 4 --aggregate always --shooterLenientEval True --delayedReward False --contextType 2 -
In a separate terminal/tab, change to the agent code directory:
cd code/dqn/ -
Then run the agent:
./run_cpu 7000 logs/tmp/
Make sure the port numbers for the server and agent match up.