SCIENCE CHINA Information Sciences, Volume 63 , Issue 11 : 212202(2020) https://doi.org/10.1007/s11432-019-2702-x

Event-based optimization with random packet dropping

More info
  • ReceivedMay 6, 2019
  • AcceptedSep 27, 2019
  • PublishedOct 9, 2020



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  • Figure 1

    Event-based optimization with random packet dropping.

  • Figure 2

    Event-based optimization without packet dropping.

  • Table 1  

    Table 1Parameter settings in the experiment

    Group $\gamma_0~$ $d^1$ $\alpha$ $\lambda$ $p$
    (1) 0.1 Average, Random, Sub-optimal 0.5 0.9 0.1
    (2) 1.0 Average, Random, Sub-optimal 0.5 0.9 0.1
    (3) 1.0 Average 0,0.1,…,1 0.9 0.1
    (4) 1.0 Average 0.5 0,0.1,…,1 0.1
    (5) 1.0 Average 0.5 0.9 0,0.1,…,1

    Algorithm 1 Gradient-based policy iteration

    Step 1. Choose $d^1~\in~\mathcal{D},~n=1$, $N$ (a fixed large integer), $\gamma_1~=~\gamma_0$, where $\gamma_0$ is a given scalar. Let $\Pi(d)$ be a $V$-by-$A$ matrix, where the $(e,j)$-th element denotes the probability for $d(e)$ to take action $j$. $\Delta>0$ is a small constant.

    Step 2. Use policy $d^n$ to generate a sample path $\{s_k,~k=0,1,\ldots\}$. The sample path should be sufficiently long to guaranteethe estimation accuracy of $g_N^{d^n}$ and $\pi^{d^n}(i|e)$.

    Step 3. Use the sample path in Step 2 to estimate