SCIENCE CHINA Information Sciences, Volume 63 , Issue 6 : 169101(2020) https://doi.org/10.1007/s11432-018-9820-3

Reinforcement learning with actor-critic for knowledge graph reasoning

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  • ReceivedJun 6, 2018
  • AcceptedJan 31, 2019
  • PublishedMay 9, 2020


There is no abstract available for this article.


This work was supported by National Natural Science Foundation of China (Grant Nos. 61590924, 61433002) and Science and Technology Innovation Action Plan Project of Shanghai Science and Technology Commission (Grant No. 18511104200).


Appendixes A and B.


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  • Table 1  

    Table 1Fact prediction results (MAP)$^{\rm~a)}$

    TasksACRLDeepPathTransETransDImprovement (%)

    a) The bold number represents the best result among the four methods for every task.


    Algorithm 1 Training procedure

    Initialize parameters $\theta$ of actor-critic network;

    for episode $\Leftarrow$ $1$ to $N$

    Initialize entity pair $\langle~e_1,e_2~\rangle$;

    while ${\rm~num\_path}~<~{\rm~max\_path}$ do

    Randomly BFS, obtain $\langle~e,~r\rangle$;

    if $r~\ne~\emptyset$ then

    Embed $\langle~e,~r\rangle$ to $\langle~s,~a\rangle$;

    Save $\langle~s,~a\rangle$ to $\varepsilon_{\rm~pos}$;

    end if

    end while

    for $\langle~s,~a\rangle$ in $\varepsilon_{\rm~pos}$


    end for

    end for

    for episode $\Leftarrow$ $1$ to $N$

    Initialize state vector $s_0$;

    while ${\rm~num\_step}~<~{\rm~max\_step}$ do

    $a_1,a_2~\sim~\pi(a|s_0)_{\rm~top2}$, $a_3~\sim~\pi(a|s_0)$;

    Choose the best action $a~\sim~R_t$;

    Save $\langle~s,~a\rangle$ to $\kappa_{\rm~pos}~/~\kappa_{\rm~neg}$;

    end while

    for $\langle~s,~a\rangle$ in $\kappa_{\rm~pos}$


    end for

    for $\langle~s,~a\rangle$ in $\kappa_{\rm~neg}$


    end for

    end for

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