Reinforcement Learning

Reinforcement Learning

 Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. In this process, the agent observes the current state of the environment and chooses an action in response. The environment then transitions to a new state and gives the agent a reward based on the action's effectiveness. The goal of the agent is to maximize the cumulative reward over time, effectively learning a policy that dictates the best actions to take from any given state. This learning paradigm is powerful for tasks where explicit supervision is unavailable, and is commonly used in areas like robotics, gaming, and autonomous vehicles, where learning through trial and error is vital