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Reinforcement learning: the future of machine learning

Reinforcement learning is a method of machine learning in which an agent learns to fulfil a specific task by interacting with its environment. The agent receives rewards or punishments for its actions, enabling it to optimise its actions and achieve goals. This method is based on the principle of trial and error, in which the agent learns to make optimal decisions through experience. Reinforcement learning has become increasingly popular in recent years due to its wide range of applications and [...]

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Deep Q-Networks: machine learning on steroids

Deep Q-Networks (DQN) are an advanced method of machine learning based on the combination of deep neural networks and Q-learning. They are designed to solve complex sequential decision problems where an agent acts in an environment and learns to perform optimal actions. DQNs use a deep neural network to approximate the Q-function, which represents the expected future utility of an action in a given state. Through iterative training

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The future of CAFM software: can artificial intelligence and AI help?

The potential of artificial intelligence and AI in the future of CAFM software is very promising. By using advanced technologies and algorithms, these tools can support and optimise intelligent decision-making processes. The integration of machine learning enables continuous improvement and adaptation to changing requirements. By utilising large amounts of data, AI-based systems can make precise predictions and provide recommendations for action, which promises more efficient use of resources, cost reduction and improved safety. The practical implementation

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