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|Number of experience episodes between each policy-updating iteration||The size of the experience buffer used to draw training data from for learning policy network weights. An episode is a period in which the vehicle starts from a given starting point and ends up completing the track or going off the track. Different episodes can have different lengths. For simple reinforcement-learning problems, a small experience buffer may be sufficient and learning will be fast. For more complex problems which have more local maxima, a larger experience buffer is necessary to provide more uncorrelated data points. In this case, training will be slower but more stable.  
 
|Number of experience episodes between each policy-updating iteration||The size of the experience buffer used to draw training data from for learning policy network weights. An episode is a period in which the vehicle starts from a given starting point and ends up completing the track or going off the track. Different episodes can have different lengths. For simple reinforcement-learning problems, a small experience buffer may be sufficient and learning will be fast. For more complex problems which have more local maxima, a larger experience buffer is necessary to provide more uncorrelated data points. In this case, training will be slower but more stable.  
 
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== Training internals ==
 
[[AWS Robomaker]]
 
  
 
== Frequently asked questions ==
 
== Frequently asked questions ==
 
[[Training FAQ]]
 
[[Training FAQ]]

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