Beyond Basics: A Journey into the World of Reinforcement Learning
Reinforcement Learning (RL) is a subfield of Machine Learning. It specifically focuses on the dynamic process of sequential decision-making and knowledge acquisition through interactions with an environment. Unlike other Machine Learning methodologies, such as supervised learning (which relies on labeled data for model training) or unsupervised learning (aimed at uncovering patterns within unlabeled data), RL distinguishes itself by employing a trial-and-error learning approach. This approach enables an agent to continually refine its actions over time, seeking to maximize long-term rewards. The analogy here can be likened to how children explore their surroundings, adapting their actions to achieve specific goals as they learn and grow.
At the core of this question lies the Markov Decision Process (MDP) — an essential mathematical framework for describing and solving Reinforcement Learning problems.
In this section, we’ll delve deep into the architecture of MDPs, which serve as the underpinning for RL algorithms.
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