December 27, 2023

The exploration-exploitation trade-off, a fundamental concept in decision-making, plays a crucial role in the field of reinforcement learning. As part of machine learning and artificial intelligence, reinforcement learning involves training agents to make decisions based on feedback from the environment. This trade-off revolves around finding the perfect balance between exploiting the best-known option and exploring […]

Reinforcement Learning, Policy Gradient Methods, AI, machine learning, deep learning, neural networks Policy gradient methods are a type of reinforcement learning technique that optimizes parametrized policies based on the expected long-term cumulative reward. These methods have advantages over traditional reinforcement learning approaches, such as the ability to handle uncertain state information and continuous states and

Reinforcement Learning. Machine Learning. Artificial Intelligence. These are the cutting-edge technologies that are revolutionizing our world. And at the heart of these innovations lies the Markov Decision Process (MDP) – a mathematical framework that enables intelligent decision-making in unpredictable environments. Examples of MDP Components: Component Description Agent The decision maker who selects actions States Set

Augmented Reality (AR) is a cutting-edge technology that merges the real world with computer-generated content, revolutionizing how we perceive and interact with our environment. By utilizing computer vision, artificial intelligence, and image processing, AR overlays virtual information onto the real world, creating immersive and interactive experiences. With AR, users can seamlessly integrate digital objects into