Method save

examples/reinforcement_learning/tutorial_DQN_variants.py:353–358

(self, path)

        return b_q - (b_r + reward_gamma * b_q_)

    def save(self, path):
        if path is None:
            path = os.path.join('model', '_'.join([alg_name, env_id]))
        if not os.path.exists(path):
            os.makedirs(path)
        tl.files.save_weights_to_hdf5(os.path.join(path, 'q_net.hdf5'), self.qnet)

    def load(self, path):
        if path is None: