Reinforcement Learning Tutorials: What is, Algorithms, Types & Examples Artificial Intelligence 2025

What is Reinforcement Learning?
Reinforcement Learning is defined as a Machine Learning method that is concerned with how software agents should take actions in an environment. Reinforcement Learning is a part of the deep learning method that helps you to maximize some portion of the cumulative reward.

This neural network learning method helps you to learn how to attain a complex objective or maximize a specific dimension over many steps.

Table of Content:

Important Components of Deep Reinforcement Learning Method
Important Components of Deep Reinforcement

Here are some important terms used in Reinforcement AI:

Agent: It is an assumed entity which performs actions in an environment to gain some reward.
Environment (e): A scenario that an agent has to face.
Reward (R): An immediate return given to an agent when he or she performs specific action or task.
State (s): State refers to the current situation returned by the environment.
Policy (π): It is a strategy which applies by the agent to decide the next action based on the current state.
Value (V): It is expected long-term return with discount, as compared to the short-term reward.
Value Function: It specifies the value of a state that is the total amount of reward. It is an agent which should be expected beginning from that state.
Model of the environment: This mimics the behavior of the environment. It helps you to make inferences to be made and also determine how the environment will behave.
Model based methods: It is a method for solving reinforcement learning problems which use model-based methods.
Q value or action value (Q): Q value is quite similar to value. The only difference between the two is that it takes an additional parameter as a current action.

How Reinforcement Learning works?
Let’s see some simple example which helps you to illustrate the reinforcement learning mechanism.

Consider the scenario of teaching new tricks to your cat

As cat doesn’t understand English or any other human language, we can’t tell her directly what to do. Instead, we follow a different strategy.
We emulate a situation, and the cat tries to respond in many different ways. If the cat’s response is the desired way, we will give her fish.
Now whenever the cat is exposed to the same situation, the cat executes a similar action with even more enthusiastically in expectation of getting more reward(food).
That’s like learning that cat gets from “what to do” from positive experiences.
At the same time, the cat also learns what not do when faced with negative experiences.
Example of Reinforcement Learning
Example of Reinforcement Learning
How Reinforcement Learning works
In this case,

Your cat is an agent that is exposed to the environment. In this case, it is your house. An example of a state could be your cat sitting, and you use a specific word in for cat to walk.
Our agent reacts by performing an action transition from one “state” to another “state.”
For example, your cat goes from sitting to walking.
The reaction of an agent is an action, and the policy is a method of selecting an action given a state in expectation of better outcomes.
After the transition, they may get a reward or penalty in return.
Reinforcement Learning Algorithms
There are three approaches to implement a Reinforcement Learning algorithm.

Value-Based
In a value-based Reinforcement Learning method, you should try to maximize a value function V(s). In this method, the agent is expecting a long-term return of the current states under policy π.

Policy-based
In a policy-based RL method, you try to come up with such a policy that the action performed in every state helps you to gain maximum reward in the future.

Two types of policy-based methods are:

Deterministic: For any state, the same action is produced by the policy π.
Stochastic: Every action has a certain probability, which is determined by the following equation.Stochastic Policy :
n{a\s) = P\A, = a\S, =S]
Model-Based
In this Reinforcement Learning method, you need to create a virtual model for each environment. The agent learns to perform in that specific environment.

Characteristics of Reinforcement Learning
Here are important characteristics of reinforcement learning

There is no supervisor, only a real number or reward signal
Sequential decision making
Time plays a crucial role in Reinforcement problems
Feedback is always delayed, not instantaneous
Agent’s actions determine the subsequent data it receives

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