Reinforcement learning

Reinforcement learning is another type of machine learning in which an AI-agent learns to take actions in an environment to maximize a reward. It learns through trial and error and receives feedback in the form of rewards or punishments. Reinforcement learning is especially suitable for scenarios where the environment is dynamic and no labeled examples are available. The goal is to develop a policy that allows the agent to select the best possible actions to optimize the overall reward.

A simple example

Imagine an AI agent that needs to navigate a maze to reach a reward, such as a piece of food. The agent starts with minimal knowledge of the maze and needs to learn which actions to take to find the reward.

At the beginning, the agent takes a random action, such as going left. If this action brings the agent closer to the reward, it receives a positive reward. If the action takes the agent further away from the reward, it receives a negative reward. The goal of the agent is to maximize the total reward over time.

The agent applies reinforcement learning by developing a policy based on the rewards and penalties it receives. It learns which actions to take in different situations to optimize the reward.

Initially, the agent may try different actions randomly and observe the associated rewards. Over time, it will recognize patterns and discover which actions are better for obtaining a positive reward. It adjusts its policy by selecting actions with higher rewards more often and avoiding actions with lower rewards.

After multiple iterations of trial-and-error, the agent learns the optimal path through the maze that leads to the reward. The reinforcement learning process helps the agent improve its policy and become more efficient in navigating the maze.

This is just a simplified example, but it illustrates how an AI agent can learn to maximize rewards through trial-and-error and adjusting its policy based on received feedback.