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pro vyhledávání: '"Noman, Nasimul"'
Actor-critic (AC) algorithms are a class of model-free deep reinforcement learning algorithms, which have proven their efficacy in diverse domains, especially in solving continuous control problems. Improvement of exploration (action entropy) and exp
Externí odkaz:
http://arxiv.org/abs/2210.00211
Electronic commerce (e-Commerce) technologies have been increased over the past two decades in different business sectors. In particular, the technologies of B2C operations have significantly improved the productivity of online small businesses such
Externí odkaz:
http://arxiv.org/abs/2110.10836
Internet of things (IoT) has significantly altered the traditional lifestyle to a highly technologically advanced society. Some of the significant transformations that have been achieved through IoT are smart homes, smart transportation, smart city,
Externí odkaz:
http://arxiv.org/abs/2110.10452
Soft Actor-Critic (SAC) is an off-policy actor-critic reinforcement learning algorithm, essentially based on entropy regularization. SAC trains a policy by maximizing the trade-off between expected return and entropy (randomness in the policy). It ha
Externí odkaz:
http://arxiv.org/abs/2109.11767
Actor-critic (AC) algorithms are known for their efficacy and high performance in solving reinforcement learning problems, but they also suffer from low sampling efficiency. An AC based policy optimization process is iterative and needs to frequently
Externí odkaz:
http://arxiv.org/abs/2108.06911
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Temperature compensation is a notable property of circadian oscillators that indicates the insensitivity of the oscillator system's period to temperature changes; the underlying mechanism, however, is still unclear. We investigated the influence of p
Externí odkaz:
http://arxiv.org/abs/1503.01862
Publikováno v:
Evolutionary Intelligence; Jun2024, Vol. 17 Issue 3, p1933-1952, 20p
Publikováno v:
In Expert Systems With Applications 1 November 2020 157
Akademický článek
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