simple q-learning
- fig/*: the figures of each experiment
- console_log/*: the console output of each experiment
- hw4.py
- python3.6
- tensorflow
- more details are writed as a
requirements.txt
file
- run an experiments
- use MODEL_NAME to specify size of model name
- use TRAIN_EPISODE to specify train times
- use STEP_LIMIT to specify the maximum possible action step in each episode
- use STATE_NUM to specify the state number size of Q-matrix
- use LEARNING_RATE to specify the learning rate
$ python3 hw4.py [-h] [-n MODEL_NAME] [-e TRAIN_EPISODE] [-l STEP_LIMIT] [-s STATE_NUM] [-lr LEARNING_RATE]
- You can use shell script to execute the models
$ bash command.sh
You can refer to jupyter notebook report.ipynb
MIT