[jira] [Created] (SPARK-6425) Add parallel Q-learning algorithm to MLLib

["zhangyouhua (JIRA)" <ji...@apache.org>]

zhangyouhua created SPARK-6425:
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             Summary: Add parallel Q-learning algorithm to MLLib
                 Key: SPARK-6425
                 URL: https://issues.apache.org/jira/browse/SPARK-6425
             Project: Spark
          Issue Type: New Feature
          Components: MLlib
    Affects Versions: 1.3.0
            Reporter: zhangyouhua


[~mengxiang]
Q-learning is a model-free reinforcement learning technique. Specifically, Q-learning can be used to find an optimal action-selection policy for any given (finite) Markov decision process (MDP). It works by learning an action-value function that ultimately gives the expected utility of taking a given action in a given state.One of the strengths of Q-learning is that it is able to compare the expected utility of the available actions without requiring a model of the environment. Additionally, Q-learning can handle problems with stochastic transitions and rewards, without requiring any adaptations.
It can be used in artificial intelligence.
we will use MapReduce for RL with Linear Function Approximation to implementation it. some detail can be find ：[https://ewrl.files.wordpress.com/2011/08/ewrl2011_submission_11.pdf]



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