Answers.Body: <p>Switching requires relearning, the network did not have a single set of weights that allowed it to play all games well. This is due to the catastrophic forgetting problem.</p>
  <p>However, recent work has been done to overcome this problem <a href="https://arxiv.org/pdf/1612.00796v1.pdf" rel="nofollow noreferrer">Overcoming catastrophic forgetting in neural networks</a>, 2016, by James Kirkpatrick et al.</p>
  
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Answers.CreationDate: 2017-04-30T03:15:23.537Z
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Answers.UserDisplayName: Nix
Answers.Body: <p>Switching required a re-learn.</p>
  
  <p>Also, <a href="https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf" rel="nofollow">note that</a>:</p>
  
  <blockquote>
    <p>We use the same network architecture, learning
    algorithm and hyperparameters settings across all seven games, showing that our approach is robust
    enough to work on a variety of games without incorporating game-specific information. While we
    evaluated our agents on the real and unmodified games, we made one change to the reward structure
    of the games during training only.</p>
  </blockquote>
  
  <p>and </p>
  
  <blockquote>
    <p>the
    network has outperformed all previous RL algorithms on six of the seven games we have attempted
    and surpassed an expert human player on three of them. </p>
  </blockquote>
  
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Answers.CreationDate: 2016-10-20T01:59:57.833Z
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Answers.UserDisplayName: Franck Dernoncourt
Body: <p>DeepMind states that its deep Q-network (DQN) was able to continually adapt its behavior while learning to play 49 Atari games.</p>
  <p>After learning all games with the same neural net, was the agent able to play them all at 'superhuman' levels simultaneously (whenever it was randomly presented with one of the games) or could it only be good at one game at a time because switching required a re-learn?</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2016-10-20T06:13:31.473Z
Comments.Text: "After learning all games with the same neural net". Does this mean same NN architecture OR same architecture and single set of weights ?
Comments.UserDisplayName: Ankur
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2016-10-20T08:05:54.047Z
Comments.Text: @Ankur actually I'm not sure - it's my (limited) understanding that they used the same architecture and didn't reset the weights between games.
Comments.UserDisplayName: Dion
Tags: <reinforcement-learning><deep-rl><dqn><deepmind><atari-games>
Title: Was DeepMind's DQN learning simultaneously all the Atari games?
UserDisplayName: Dion
ThreadText: # Was DeepMind's DQN learning simultaneously all the Atari games?
  
  (Asked by: username_0 on 2016-10-20)
  
  DeepMind states that its deep Q-network (DQN) was able to continually adapt its behavior while learning to play 49 Atari games.
  
  After learning all games with the same neural net, was the agent able to play them all at 'superhuman' levels simultaneously (whenever it was randomly presented with one of the games) or could it only be good at one game at a time because switching required a re-learn?
  
  **Tags:** reinforcement-learning, deep-rl, dqn, deepmind, atari-games
  
  > **Comments:**
  > **username_1**: "After learning all games with the same neural net". Does this mean same NN architecture OR same architecture and single set of weights ?  
  > **username_0**: @username_1 actually I'm not sure - it's my (limited) understanding that they used the same architecture and didn't reset the weights between games.  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_3 on 2016-10-20. Score: 3 | Accepted answer
  
  Switching required a re-learn.
  
  Also, [note that](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf):
  
  > We use the same network architecture, learning
  > algorithm and hyperparameters settings across all seven games, showing that our approach is robust
  > enough to work on a variety of games without incorporating game-specific information. While we
  > evaluated our agents on the real and unmodified games, we made one change to the reward structure
  > of the games during training only.
  
  and
  
  > the
  > network has outperformed all previous RL algorithms on six of the seven games we have attempted
  > and surpassed an expert human player on three of them.
  
  
  ---
  
  ### Answer by username_2 on 2017-04-30. Score: 2
  
  Switching requires relearning, the network did not have a single set of weights that allowed it to play all games well. This is due to the catastrophic forgetting problem.
  
  However, recent work has been done to overcome this problem [Overcoming catastrophic forgetting in neural networks](https://arxiv.org/pdf/1612.00796v1.pdf), 2016, by James Kirkpatrick et al.
  
  
  ---
  
  