Model-free (reinforcement learning)
Imagine you are learning to ride a bike. You start without any knowledge of how to balance or pedal, and nobody tells you how to do it. Model-free reinforcement learning is a bit like this - you don't have a "model" or blueprint to follow, but you learn from the consequences of your actions.
Basically, in model-free reinforcement learning, an agent (like you on your bike) tries different actions in an environment and learns which actions lead to good outcomes (like successfully riding your bike without falling off) and which actions lead to bad outcomes (like crashing into a bush). The agent doesn't have any preconceived ideas about the environment or what it should do, but instead learns from its own experiences.
The agent gets rewards or punishments based on its actions - for example, successfully riding the bike might earn a reward, while crashing might result in a punishment. The agent will then adjust its behavior based on these rewards and punishments in order to maximize its future rewards.
Over time, the agent develops a better understanding of the environment and how to act in it to achieve its goals. It might start out just trying random actions, but will eventually learn which actions work best and use this knowledge to make better decisions in the future.
Basically, in model-free reinforcement learning, an agent (like you on your bike) tries different actions in an environment and learns which actions lead to good outcomes (like successfully riding your bike without falling off) and which actions lead to bad outcomes (like crashing into a bush). The agent doesn't have any preconceived ideas about the environment or what it should do, but instead learns from its own experiences.
The agent gets rewards or punishments based on its actions - for example, successfully riding the bike might earn a reward, while crashing might result in a punishment. The agent will then adjust its behavior based on these rewards and punishments in order to maximize its future rewards.
Over time, the agent develops a better understanding of the environment and how to act in it to achieve its goals. It might start out just trying random actions, but will eventually learn which actions work best and use this knowledge to make better decisions in the future.