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Reinforcement Learning and its Application

What is Reinforcement Learning and its Application? Reinforcement learning (RL) is a semi-supervised algorithm which permits machines and software agents to automatically calculate the ideal performance within a given context, to increase its overall performance. This technique allows an agent to take actions and interact with an environment, learn from that interaction and then maximize the total rewards. RL, is about taking decisions. In other words, we can roughly assume that the output totally depends on the state of the input and the next input depends on the output of the given input.

Reinforcement learning (RL) is different from the other machine learning methods in a way that in other techniques, the algorithm is not clearly told how to do a task, but works over the problem by its own.

In Supervised machine learning technique, we have input and output in the form of labeled data that we used to train our system. Whereas in RL, there is no training data that the reinforcement agent chooses what steps to follow to perform the given task. There is no training dataset available for agent, it is bound to learn from its experience.

Reinforcement Learning and its Application in Artificial Intelligence

Reinforcement Learning and its Application in AI consists of a collection of computational approaches that are primarily motivated by their potential for resolving practical problems. RL encouraged by behaviorist psychology. It is almost similar to how a child learns to do a new job. As an AI robot, which could be an automatic car or a program playing chess game, perform interaction with given environment, collects a reward state which depending on how it accomplishes, such as driving to goal safely or winning a chess game. On the other hand, the agent takes a penalty for incorrect performance, such as checkmated or going out of the way.

Reinforcement Learning includes these simple phases:

  • Deeply Observing the given situation/environment 
  • Determining how to perform by some approach
  • Act according to the environment 
  • Getting a penalty or reward
  • Learn from the past practices and filtering our approach
  • Repeat till a best approach found

Reinforcement Learning and its Application Advantages in AI

The AI agent over every small time period makes some decisions to take full advantage of its reward and lessen its penalty by dynamic programming. The benefit of this method to artificial intelligence is that it permits an AI based program to improve its knowledge and learn without a programmer instruction to how an agent complete the task.

Reinforcement Learning Algorithm

There are different algorithms that used in reinforcement learning. We are discussing Q-learning algorithm for understanding.

Q-Learning Algorithm:

Q-Learning is a simplest method of Reinforcement Learning which uses action values and Q-values to increase the overall performance of the agent. It allows the agent interact with the environment and learn from the reward and penalty in given environment with time, that how to perform best action in a given state. The main steps of Q learning Algorithm are:

Action values and Q-values

These values are estimation that, what action agent take to reach the goal state. Q (S, A) are defined as action and states.

Price and Episodes:

The agent takes a number of activities from the present state to reach the goal state. These activities created on number of actions which agent take while interacting with environment. The price and penalty added after each action.  The agent reaches the goal state and complete his one episode successfully.

Temporal difference

        This rule is used to estimate the Q value after each action of agent.  

            S: Current state of the agent

            A: Actions which agent take

            S: Next state where agent have to move

            A: By using current Q states pick up the next action.

            R: Current Reward of agent

α: Length of steps taken to update the estimation function 

ϒ: Discovering factor for future reward

Choosing the suitable action to take using ꓰ-Greedy approach

        This approach chooses the action using Q-value estimation.

  • Choose the actions which have highest Q-values 
  • Choose the actions at random

Reinforcement Learning Example:

In this example we have a goal state and an agent robot, with several obstacles between Goal state and agent. The agent has to discover the best available route to find the goal.  

The given figure displays robot agent, fire and prize diamond. The main aim of the agent robot to get the prize which is diamond and have to avoid the obstacles fire. The robot increases his knowledge by trying available pathways and picking the route which provide him prize with the minimum obstacle. Every correct footstep will add the robot agent a prize and each incorrect footstep will deduct the prize of robot agent. The total prize will be considered when it comes to the final prize which is diamond.

Main points in Reinforcement learning 

Start State: 

A primary state from where agent will initiate its journey.

End State: 

There are various possible ways and multiple solution available for a specific problem


The training of agent is constructed on the start state; the agent will be coming back a state and the operator will choose to give prize or punish the agent totally depend on its End state. The agent continues to learn from experience.


The good results are totally based on the maximum prize.

Reinforcement learning and its Applications

Reinforcement learning have many practical applications. Its successfully implemented in a large number of areas. 

  • In industrial automation robotics control system are developed using reinforcement learning.
  • RL use to solve combinatorial exploration problems which are games which are playing by computers
  • Data processing and Machine learning
  • RL, is used to make training systems which provide instruction and resources according to student’s requirement 
  • In Energy consumption optimization
  •  Video games

Reinforcement learning python implementation using Q-learning 

In this implementation, we try to teach a bot to reach its goal using the Q-Learning technique. The main steps in python that should be followed are given below.

Step1:  Importing the compulsory libraries in python

Step2: Initialize Q-table

Step3: Make the training procedure that will bring up-to-date this Q-table 


Step4: Q-table has been created over 100,000 episodes

Step5: Evaluating the agent

In the step we Evaluate the performance of our agent.


By using this code, we can apply the Reinforcement learning and measure the performance of our agent.



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