Deep Exploration via Bootstrapped DQN
Deep Exploration via Bootstrapped DQN

    
    
                
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            Abstract:Efficient exploration in complex environments remains a major challenge for reinforcement learning. We propose bootstrapped DQN, a simple algorithm that explores in a computationally and statistically efficient manner through use of randomized value functions. Unlike dithering strategies such as epsilon-greedy exploration, bootstrapped DQN carries out temporally-extended (or deep) exploration; this can lead to exponentially faster learning. We demonstrate these benefits in complex stochastic MDPs and in the large-scale Arcade Learning Environment. Bootstrapped DQN substantially improves learning times and performance across most Atari games.
    

    
    
  
      Submission history From: Ian Osband [view email]                  [v1]
        Mon, 15 Feb 2016 10:54:20 UTC (5,872 KB)
            [v2]
        Fri, 1 Jul 2016 16:23:55 UTC (7,516 KB)
    [v3]
        Mon, 4 Jul 2016 17:11:52 UTC (7,516 KB)
