<p>What you are describing is <em>not</em> <a href="https://arxiv.org/pdf/1509.06461.pdf" rel="nofollow noreferrer">Double DQN</a>. The periodically updated target network is a core feature of the original DQN algorithm (and all of its derivatives). <a href="https://www.nature.com/articles/nature14236.pdf" rel="nofollow noreferrer">DeepMind's classic paper</a> explains why it is crucial to have two networks:</p>

<blockquote>
  <p>The second modification to online Q-learning aimed at further improving the
  stability of our method with neural networks is to use a separate network for generating the targets <code>y_j</code> in the Q-learning update. More precisely, every <code>C</code> updates we
  clone the network <code>Q</code> to obtain a target network <code>Q^</code> and use <code>Q^</code> for generating the
  Q-learning targets <code>y_j</code> for the following <code>C</code> updates to <code>Q</code>. This modification makes the algorithm more stable compared to standard online Q-learning, where an update
  that increases <code>Q(s_t, a_t)</code> often also increases <code>Q(s_{t+1}, a)</code> for all <code>a</code> and hence also increases the target <code>y_j</code>, possibly leading to oscillations or divergence of the policy. <strong>Generating the targets using an older set of parameters adds a delay between the time an update to <code>Q</code> is made and the time the update affects the targets <code>y_j</code>, making divergence or oscillations much more unlikely.</strong></p>
</blockquote>
DOUBLE DQN doesn't make any sense
<p>Why use 2 networks, train once every episode and update target network every <strong>N</strong> episode, when we can use 1 network and train it ONCE every <strong>N</strong> episode! there is literally no difference!</p>