Deep Reinforcement Learning (DRL) techniques have allowed deep neural networks (DNN) based AI to move from theoretical concepts in the research lab to real-life, business solutions. Although tools such as the Microsoft Project Bonsai toolchain have tremendously streamlined, accelerated, and simplified the design, training, and deployment of Autonomous Systems AI agents, it does not mean they suddenly became simple…
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. Using the self-training mechanism of deep reinforcement learning,…
Before diving deep into the concept of using simulators to solve Reinforcement Learning (RL) real-world problems, let’s understand the basics of RL. What is Reinforcement Learning? Reinforcement Learning (RL) is an…
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…
What is Reinforcement Learning? Deep Reinforcement Learning (DRL) or simply Reinforcement Learning (RL) is an area of machine learning that focuses on the training and decision-making abilities of AI agents.…
Autonomous Systems can solve many business problems by bringing AI from research labs to real-life use cases. Autonomous Systems leverage Deep Reinforcement Learning (DRL) techniques to train AI agents using…
