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From "Sean Owen (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (SPARK-6425) Add parallel Q-learning algorithm to MLLib
Date Mon, 11 Jan 2016 10:20:39 GMT

     [ https://issues.apache.org/jira/browse/SPARK-6425?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Sean Owen resolved SPARK-6425.
------------------------------
    Resolution: Won't Fix

> 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
>
> [~mengxr]
> 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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