if done:
print("Epoch %d Train Game %d get %.1f" %
(epoch, games_cnt, game.get_total_reward()))
break
if SAVE_MODEL and games_cnt % 100 == 0:
saver.save(SESSION, model_savefile)
print("Saving the network weigths to:", model_savefile)
print("\nTesting...")
test_scores = []
for test_step in range(EPISODES_TO_TEST):
game.reset()
agent.reset_cell_state()
while not game.is_episode_finished():
state = game.get_state()
action = agent.act(state, train=False)
game.make_action(action)
test_scores.append(game.get_total_reward())
test_scores = np.array(test_scores)
if SAVE_MODEL:
saveScore(test_scores)
if test_scores.mean() > max_avgR:
max_avgR = test_scores.mean()
saver.save(SESSION, max_model_savefile)
'''
print("TIME TO WATCH!!")
# Reinitialize the game with window visible
