by Prathmesh Salunkhe
In the world of Reinforcement Learning (RL), we often start with a simple picture: an agent interacts with an environment. The agent takes an action, the environment responds with a new observation and a reward, and this loop repeats. The agent’s goal is to learn a strategy that maximizes its total reward over




