<p>In the paper that you mentioned, 49 networks are trained for 49 games: "A different network was trained on each game: the same network architecture, learning algorithm and hyperparameter settings (see Extended Data Table 1) were used across all games, showing that our approach is robust enough to work on a variety of games while incorporating only minimal prior knowledge", which is quoted from the paper.</p>

<p>There are algorithms which only train one network for all 49 games, e.g. <a href="https://arxiv.org/pdf/1809.04474.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1809.04474.pdf</a></p>
Deep Reinforcement Learning with Atari Games: one DQN for all games or 49 DQNs for 49 games?
<p>I would like to have a clarification about the article "Human level control through deep reinforcement learning" in Nature 2015. When I read it, I understand that they use a DQN with the same algo, network architecture and hyperparameters. Great! But they don't specify if they train each game from scratch and as a result we obtain one neural network per game (means 49 neural networks for the 49 games) or if they train all the game with a unique neural network (means only one neural network can play 49 games). </p>

<p>Is there someone who know what is the correct answer? Because it is not the same thing at all!:)</p>

<p>Thanks,</p>