Reinforcement learning is an optimization method that finds a policy to control an agent's behavior by utilizing a well-designed reward system, encouraging the agent to take positive actions.
In reinforcement learning, an agent takes specific actions in an environment
according to a policy. The state of the environment changes
depending on the action, and the agent receives a reward based on
whether this change in state is positive or negative.
Components of Reinforcement Learning
If the policy determining the agent's actions is
excellent, the environment will continue to improve, and the rewards will grow
accordingly. The goal of reinforcement learning is to accumulate all the
rewards received from actions and find a policy that maximizes this cumulative
reward. In simple terms, reinforcement learning aims to find the best policy,
and the best policy maximizes the total accumulated reward.
Basic Concepts of Reinforcement Learning (https://pixabay.com/)
Think of a child crossing a flower bed without being
able to see the ground but only looking straight ahead. If the child
successfully avoids stepping on any flowers, none will be crushed, but if the
child missteps, many flowers will be trampled.
Imagine that each time the child takes a step without crushing a flower, they
are praised, but if they crush a flower, they are scolded. After countless
attempts, the child will eventually be able to cross the flower bed without
stepping on any flowers. This is because the child accumulates memories of
being praised for certain steps and can use this experience to know where to
step to avoid the flowers.
In this example:
- The flower bed is the environment,
- The flowers are the state,
- The praise and scolding are the rewards,
- The child is the agent, and the steps taken by the child are
the actions.
As we grow, we learn many things through reinforcement
learning without even realizing it. Whether it's learning to walk, speak, or
ride a bike, these skills are naturally ingrained through a reward system of
successes and failures, praise and scolding, pain, and a sense of achievement.
When the number of actions and states is small, it is possible to calculate the
optimal policy. However, as the number of actions and states increases, finding
the optimal policy through calculations becomes difficult. In such cases,
artificial neural networks are used.
While the concept of reinforcement learning introduced so far may seem abstract
or unfamiliar, as we delve deeper into the necessary concepts, you will be able
to make it your own knowledge without much difficulty.
