The Data Science Doctor explains how to use the reinforcement learning branch of machine learning with the Q-learning approach, providing code on how to solve a maze problem for an easy-to-understand ...

Introduction What is Q-learning? Q-learning is a type of reinforcement learning algorithm that teaches agents how to act in a given environment to maximise rewards over time.

In recent years, machine learning (ML) algorithms have proved themselves to be remarkably useful in helping people deal with different tasks: data classification and clustering, pattern revealing ...

Since the news of Q* broke, many researchers outside OpenAI have speculated about whether the name is a reference to other existing techniques within the field, such as Q-learning, a technique for ...

The battle at OpenAI was possibly due to a massive breakthrough dubbed Q* (Q-learning). Q* is a precursor to AGI. What Q* might have done is bridged a big gap between Q-learning and pre-determined ...

Unlike basic Q-learning algorithms, which generally focus on finding the optimal path to maximize rewards, the modified bandit Q-learning algorithm aims to learn the optimal Q value for every ...

A special category of algorithms, machine learning algorithms, try to “learn” based on a set of past decision-making examples.