play CartPole to 200 step with Policy Gradient using TensorFlow2. this code is refined from Aurelien Geron's <Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow>.
policygradient_cartpole_train.py : train models ,write these models in "model-save-path" folder;
policygradient_cartpole_test.py : test models ,read models from "model-save-path" folder ,test these model with several episodes ,write a result csv in "result-save-path"folder;
pg_cartpole_0300.h5 ：a good model plays well;
pg_cartpole_test_result.csv : an example of result csv;
(1) give a folder to reserve the trained models # default : ./
(2) python3 policygradient_cartpole_train.py #default parameters is recommended
python3 policygradient_cartpole_test.py
(1) when testing , you can use "--render=True" to show the cartpole,but it will be slow.
(2) the model will saved every 20 iteration(default),the example max-step curve shows below:
