ABSTRACT
The purpose of the research is to explore and develop Deep Reinforcement Learning and Q-Learning algorithms in
order to improve Ethereum cybersecurity in contract vulnerabilities, the smart contract market and research
leadership in the area. Deep Reinforcement Learning (Deep RL) is gaining popularity among AI researchers due to
its ability to handle complex, dynamic, and particularly high-dimensional cyber protection problems. The
benchmark of RL is goal-oriented behavior that increases rewards and decreases penalties or losses, and enhances
real-time interaction between an agent and its surroundings. The research paper examines the three major
cryptocurrencies (Bitcoin, Litecoin and Ethereum) and the role played by cyber-attacks.The Design Science
Research Paradigm as applied in Information Systems research was used in this research, as it is hinged on the idea
that information and understanding of a design problem and its solution are attained in the crafting of an
artefact. The proposed constructs were in the form of Deep Reinforcement Learning and Q-Learning algorithms
designed to improve Ethereum cybersecurity. Smart contracts on the Ethereum blockchain can automatically
enforce contracts made between two unknown parties. Blockchain (BC) and artificial intelligence (AI) are used
together to strengthen one another's skills and complement one another. Consensus algorithms (CAs) of BC and
deep reinforcement learning (DRL) in ETS were thoroughly reviewed. In order to integrate many DCRs and
provide grid services, this article suggests an effective incentive-based autonomous DCR control and management
framework. This framework simultaneously adjusts the grid's active power with accuracy, optimizes DCR
allocations, and increases profits for all prosumers and system operators. The best incentives in a continuous action
space to persuade prosumers to reduce their energy consumption were found using a model-free deep deterministic
policy gradient-based strategy. Extensive experimental experiments were carried out utilizing real-world data to
show the framework's efficacy.
Keywords: - Reinforcement Learning; DRL; Double Q-Learning; Blockchain; Ethereum blockchain; Cryptocurrencies; ECC; DNS