IEEE Consumer Electronics Magazine - July/August 2021 - 87

Figure 5. QUC-learning-Evaluation
Figure 3. Gradient ascent RL algorithms-
comparative evaluation.
which plots the ratio of the normalized reward
to normalized real execution time, where normalization
is performed by dividing the sum of
the rewards by time. The results reveal that the
LRI algorithm outperforms the LRP and LR-P
algorithms due to the extremely low real execution
time and achieved rewards.
AI-Enabled Slave TTPs Offloading
A comparative evaluation among the gradient
ascent, log linear, and QUC RL algorithms is carried
out here to determine the effectiveness of
Slave TTP offloading strategies. Figure 4 presents
the convergence of the LRI, BLLL, MLLL, and QUC
algorithms. The results reveal that the log linear
(BLLL, MLLL) and QUC
algorithms require more
iterations than the LRI algorithm to converge to a
stable decision, as they explore more thoroughly
the available strategies or converge to the best
equilibrium, respectively, while achieving higher
TTPMs' average rewards. Also, it is clear that the
MLLL algorithm converges faster when compared
to BLLL given the form of the probabilistic
update rule [see (4c) and (4d)]. A higher value of
the learning parameter b for the log linear algorithms
results in higher TTPMs' average rewards
at the expense of longer execution times.
Figure 6 shows the results for the QUC
algorithm.
Figure 6(a) illustrates the uncertainty
associated with a TTPM offloading strategy,
showing two strategies where the TTPM converged,
i.e., strategy #3 and #9, as well as the
number of times that each strategy is selected
before convergence occurs. The results reveal
that strategies which are selected more often
produce lower uncertainties, as the TTPM has
explored the solution space more thoroughly.
This is confirmed by the corresponding Q-value
for these two strategies [see Figure 6(b)], which
shows the strategy that the TTPM finally selects
generates a greater Q-value (reward).
Comparative Evaluation
In this section, the gradient ascent, log linear,
and QUC RL algorithms are compared with
respect to the TTPMs' average reward, real execution
time, and the ratio of the normalized average
reward over the normalized real execution
time [see Figure 5] when tasked with selecting
the fractional amounts to offload to the Slave
TTPs. The results reveal that the QUC
algorithm
Figure 4. Gradient ascent, log linear, Q-learning RL
algorithms-Average rewards.
July/August 2021
outperforms the other algorithms in terms of
average achieved reward, at the expense of
higher real execution time. The MLLL algorithm
ranks second and performs better than the BLLL
algorithm when using the same learning parameter
b - with respect to the achieved reward and
execution time. In contrast, the LRI algorithm
87

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