Reinforcement Learning

Reinforcement Learning explores a fundamentally different learning paradigm from supervised learning.

Instead of receiving labeled examples describing the correct output, an agent interacts with an environment, takes actions, receives rewards, and gradually learns which decisions lead to better long-term outcomes.

My exploration began with the mathematical foundation of value functions and the Bellman equation before progressing toward Q-learning and Deep Q-Networks.

The central question throughout this topic is:

How can an agent choose actions now when their real value may only become apparent several steps in the future?

Understanding value iteration, Q-values, exploration versus exploitation, and function approximation helped build the conceptual bridge from classical reinforcement learning toward deep reinforcement learning.