DQN+Active Learning

本文主要是介绍DQN+Active Learning，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

关于MarkDown公式详细编辑可以参考博客

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Initialize replay memory $M$ to capacity $N$
Initialize action-value function $Q$ with random weights
for episode = $1, 2,...,N$ do
    $D_l$ ← model and shuffle $D$
    $\phi$ ← Random
    for $i = 1$, $|D|$ do
        Construct the state $si$ using $x_i$
        With probability $\epsilon$ select a random action $a_i$
        Otherwise select $a_i = arg$ $maxQ^\pi(s_i,a;\theta)$
        if $a_i$ = 1 then
            Obtain the annotation $y_i$
            $D_l$←$D_l + (x_i,y_i)$
            Updata model $\phi$ based on $D_l$
        end if
        Receive a reward $r_i$ from test data
         if $|D_l|$= $B$ then
            Store transition $(s_i,s_i,r_i,Terminate)$ in $M$
            Break
        end if
        Construct the new state $s_{i+1}$
        Store transition $(s_i,s_i,r_i,s_{i+1})$ in $M$
        Sample random minibatch of transitionsfrom $M$, and perform gradient descent step on $L(θ)$
        Update policy with $\theta$
    end for
end for

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