The traditional AI training method is rarely applicable in real-life business use cases simply because it requires an amount of data seldom available to businesses. However, AI agents can also…
Optimizing bed allocation in hospitals based on how patients (randomly) check in is a well-known and complex challenge for which multiple techniques have been used throughout the decades. So much…
As companies look for ways to put their data to use, reinforcement learning (RL) is becoming an increasingly inviting and accessible option. General purpose RL tools – such as Microsoft’s…
In Deep Reinforcement Learning (DRL), an agent needs to interact with the environment (either physical or simulated) by performing actions to obtain rewards. The agent’s goal is to maximize its rewards and learns by adjusting its policy (the agent’s strategy) based on…