def load(self, name):
self.model.load_weights(name)
def save(self, name):
self.model.save_weights(name)
if __name__ == "__main__":
env = gym.make('CartPole-v1')
state_size = env.observation_space.shape[0]
# print(state_size) # 4 =: left , right , balance , slide or drop the pole
action_size = env.action_space.n
# print(action_size) # 2
agent = DQNAgent(state_size, action_size)
smodel = agent._build_model()
smodel.summary()
# agent.load("cartpole-dqn.h5")
done = False
batch_size = 32
for e in range(EPISODES):
# reset state in the beginning of each game
state = env.reset()
state = np.reshape(state, [1, state_size]) # turn the state into a one dimensional matrix which is a vector
# time represents each frame of the game
# Our goal is to keep the pole upright as long as possible until score of 500
# the more time the more score
for time in range(700):
env.render()
# Decide action
