Answers.Body: <p>I'm not familiar with the ins and outs of self-driving cars, but I imagine that the action space is not discrete. For instance, the car may want to decide what angle it needs to turn (rather than left or right). The update in Q-Learning involves taking <span class="math-container">$\max_aQ(s, a)$</span>; this is theoretically possible for a continuous action space, but it would itself require some expensive optimisation at each time step to find the maximum. It is more likely that if RL were to be applied to self-driving cars it would be through a method that easily allows for a continuous action space, like the methods detailed in <a href="https://arxiv.org/pdf/1509.02971.pdf" rel="nofollow noreferrer">this</a> paper.</p>
  <p>I found <a href="https://arxiv.org/pdf/2002.00444.pdf" rel="nofollow noreferrer">this</a> survey of Deep RL for autonomous driving that you may want to look at.</p>
  
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Answers.CreationDate: 2020-05-12T21:49:20.023Z
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Body: <p>Why can't DQN be used for self-driving cars?
  Why can't DQN and similar RL algorithms be used for self-driving cars?</p>
  
  <p>The reason why I am curious is that it successfully plays go and other multistate games.</p>
  
Tags: <reinforcement-learning><dqn><deep-rl><applications><autonomous-vehicles>
Title: Why can't DQN be used for self-driving cars?
UserDisplayName: dato nefaridze
ThreadText: # Why can't DQN be used for self-driving cars?
  
  (Asked by: username_0 on 2020-05-09)
  
  Why can't DQN be used for self-driving cars?
  Why can't DQN and similar RL algorithms be used for self-driving cars?
  
  The reason why I am curious is that it successfully plays go and other multistate games.
  
  **Tags:** reinforcement-learning, dqn, deep-rl, applications, autonomous-vehicles
  
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  ## 1 answers
  
  ### Answer by username_1 on 2020-05-12. Score: 3
  
  I'm not familiar with the ins and outs of self-driving cars, but I imagine that the action space is not discrete. For instance, the car may want to decide what angle it needs to turn (rather than left or right). The update in Q-Learning involves taking $\max\_aQ(s, a)$; this is theoretically possible for a continuous action space, but it would itself require some expensive optimisation at each time step to find the maximum. It is more likely that if RL were to be applied to self-driving cars it would be through a method that easily allows for a continuous action space, like the methods detailed in [this](https://arxiv.org/pdf/1509.02971.pdf) paper.
  
  I found [this](https://arxiv.org/pdf/2002.00444.pdf) survey of Deep RL for autonomous driving that you may want to look at.
  
  
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