While training a pet, you reward them with every correct response. You can follow the same reward-based training for software or robot, so the program effectively performs tasks. Reinforcement learning is a unique artificial intelligence technique that enables you to train your machines with the help of a machine learning algorithm. Let’s start the Q-learning algorithm’s journey in detail. Moreover, we can discover how the world of reinforcement learning works.

Reinforcement Learning

You can use reinforcement learning in machine learning and enhance the performance of your model. With RL, your model performs various activities as you maximize the reward. This technique involves different machines and software so your model can develop the perfect behavior or direction in a particular situation.
Supervised learning and reinforcement learning are different techniques. In supervised learning, the training data works as the answers to the solution. These types of models already include the correct answers. However, in reinforcement learning, the algorithm does not include the correct answers, but agents decide how to take actions and perform various functions according to the task. The machine learns from experience without seeking help from training data.

What is Q-Learning?

Q-learning is a value-based learning algorithm and focuses on optimizing the value function according to the environment or problem. Q in the Q-learning represents quality with which the model finds its next action improving the quality. The process can be automatic and straightforward. This technique is amazing to start your reinforcement learning journey. The model stores all the values in a table, which is the Q Table. In simple words, you use the learning method for the best solution. Below, you will learn the learning process behind a Q-learning
model.

Learning Process of Q-Learning

The following example of a game will help you understand the concept of Q-learning:

1. Initialization

Your agent on playing the game for the first time will not include any knowledge. So we will assume the Q table to be zero.

2. Exploration or Exploitation

In this step, your agent will choose anyone from the two possible ways. If the agent exploits, it will collect information from the Q Table, or when the agent explores, it will try to make new ways.
• When your agent works for a higher number for a while, it is essential to exploit.
• When your agent does not have any experience, exploring is essential.
You can handle the adjustments among two conditions, exploration and exploitation, by adding an epsilon. Include the epsilon on the value function. When we start with the model and do not include any information, you should prefer exploration. However, once your model starts adapting to the environment, you need to follow exploitation. In simple words, the agent will take action in step two, and the choices are exploration and exploitation.

3. Measure Reward

When the agent decides what action to choose, it acts. This leads the agent to the next step, which is State “S.”In this state, the agent performs four actions. Each of these actions will direct the agent to various reward scores. For instance, if the agent chooses state five from state 1, it will move further based on that state’s experience. The agent can now choose to move to State 6 or State 9 depending on the previous experience and possible reward expectation.

4. Update Q table

The agent will calculate the reward value. The algorithm will use Bellman’s equation to update the value at State “S.” Here are some terminologies
Learning Rate–Learning rate is a constant that determines the weight you need to add in the Q-Table for generating a new value instead of the old one.
Discount Rate–Discount rate is the constant. It discounts about what will be the future reward. In simple words, discount rate helps in balancing the effect of upcoming rewards on the new values.
Once the agent goes through all these steps learning significantly, it will achieve updated values on Q-Table. Now, it is simple to use the Q-Table as mapping the states. Every state agent will select an action leading it to the state with the highest Q value.

Deep Q Learning

Deep Q Learning can help the model directly update the Q-table with appropriate values and perform the tasks more efficiently. However, you need to consider the model’s complexity as a complex environment that can significantly decrease performance.
On the other hand, the time and resources will balance the model’s infeasibility and inefficiency while modifying and updating the Q-Table with appropriate values. Deep Q Learning enables you to use the Q-Learning strategy by integrating the artificial neural networks.

How Deep Q Learning Works

You can increase the model’s efficiency by estimating the perfect Q-function with a function approximator’s help. Use this technique instead of using value integrations for directly computing the Q-values. The best method to choose right now is the application of artificial neural networks.
A neural network will help the agent choose the state by receiving the input. These inputs are the states from the environment. After receiving the input, the neural network will estimate the Q-value. The agent will make decisions based on these Q-values.
We can calculate the loss by comparing the target value and the model’s output. This is possible once we choose the target value. We need to use Bellman Equation for that:
Q*(s,a) = E [R t + 1 + γmaxa′q∗(s′,a′)
Now, we will use stochastic gradient descent and backdrop algorithm, so an artificial neural network updates the value, minimizing errors. You should know that if you have a small state space, you need to use standard Q-Learning instead of Deep Q Learning. Q-Learning will compute optimal values faster and efficiently with small state space.

Conclusion

Reinforcement learning is about solving how an agent will learn in an uncertain environment by making various sequences of decisions. Some numerous techniques and methods enable the agent to determine its path and make progressive actions. One of these reinforcement learning techniques is Q-learning. Q-learning is currently popular because this strategy is model-free.
You can also support your Q-learning model with Deep Learning. Deep Learning includes numerous artificial neural networks that identify the suitable weights to find the best possible solution. A Q-learning with Neural Networks is Deep QLearning. With these techniques, businesses achieve numerous advancements in making decisions and performing tasks.