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Reinforcement learning (RL) is a type of ML, which involves learning from interactions with the environment to accomplish certain long-term objectives connected to the environmental condition. RL takes place when action sequences, observations, and rewards are used as inputs, and is hypothesis-based and goal-oriented. The key asynchronous RL algorithms are Asynchronous one-step Q learning, Asynchronous one-step SARSA, Asynchronous n-step Q-learning and Asynchronous Advantage Actor-Critic (A3C). The paper ascertains the Reinforcement Learning (RL) paradigm for cybersecurity education and training. The research was conducted using a largely positivism research philosophy, which focuses on quantitative approaches of determining the RL paradigm for cybersecurity education and training. The research design was an experiment that focused on implementing the RL Q-Learning and A3C algorithms using Python. The Asynchronous Advantage Actor-Critic (A3C) Algorithm is much faster, simpler, and scores higher on Deep Reinforcement Learning task. The research was descriptive, exploratory and explanatory in nature. A survey was conducted on the cybersecurity education and training as exemplified by Zimbabwean commercial banks. The study population encompassed employees and customers from five commercial banks in Zimbabwe, where the sample size was 370. Deep reinforcement learning (DRL) has been used to address a variety of issues in the Internet of Things. DRL heavily utilizes A3C algorithm with some Q-Learning, and this can be used to fight against intrusions into host computers or networks and fake data in IoT devices.
Reinforcement learning (RL) is a type of ML, which involves learning from interactions with the environment to accomplish certain long-term objectives connected to the environmental condition. RL takes place when action sequences, observations, and rewards are used as inputs, and is hypothesis-based and goal-oriented. The key asynchronous RL algorithms are Asynchronous one-step Q learning, Asynchronous one-step SARSA, Asynchronous n-step Q-learning and Asynchronous Advantage Actor-Critic (A3C). The paper ascertains the Reinforcement Learning (RL) paradigm for cybersecurity education and training. The research was conducted using a largely positivism research philosophy, which focuses on quantitative approaches of determining the RL paradigm for cybersecurity education and training. The research design was an experiment that focused on implementing the RL Q-Learning and A3C algorithms using Python. The Asynchronous Advantage Actor-Critic (A3C) Algorithm is much faster, simpler, and scores higher on Deep Reinforcement Learning task. The research was descriptive, exploratory and explanatory in nature. A survey was conducted on the cybersecurity education and training as exemplified by Zimbabwean commercial banks. The study population encompassed employees and customers from five commercial banks in Zimbabwe, where the sample size was 370. Deep reinforcement learning (DRL) has been used to address a variety of issues in the Internet of Things. DRL heavily utilizes A3C algorithm with some Q-Learning, and this can be used to fight against intrusions into host computers or networks and fake data in IoT devices.
ObjectivesInsurTech is a new and interesting phenomenon, linked to the use of new technologies, such as artificial intelligence or distributed ledger technology, in the insurance sector. The factual and legal nature of relationships in this area, due to their heterogeneous and complex international character, raises many questions. Some of these relate to private international law, where conflict situations raise questions such as which law (the law of which state) is the substantive law applicable to resolve these situations. Presenting the complexity of this area, this statement describes possible solutions and reflects on the need and potential of applying private international law in the InsurTech sector. It introduces the InsurTech phenomenon, presents its links with private international law, reflects on the adaptability of existing mechanisms of this law to highly technological legal relations, and concludes by an attempt to indicate how to combine InsurTech and private international law, and whether this is possible at all.Material and methodsThe work was written using standard scientific methods for legal science. It is primarily a dogmatic work, but also reaches for comparative legal elements. This choice of methods is justified by the presented issues.ResultsThe result of the research is an assessment as to the possible use of private international law tools for the problems that arise with InsurTech instruments.ConclusionsThe author points out that the currently known private international law instruments are not suited to the modern requirements of the insurance services sector, especially in the context of the use of artificial intelligence or DLT technology (blockchain, smart contract) in the sector.
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