Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment to maximize the notion of cumulative reward. It is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.

The environment is typically stated in the form of a Markov decision process (MDP) because many reinforcement learning algorithms for this context use dynamic programming techniques. The main difference between the classical dynamic programming methods and reinforcement learning algorithms is that the latter does not assume knowledge of an exact mathematical model of the MDP and they target large MDPs where exact methods become infeasible.

HOW DOES RL WORK? In reinforcement learning, developers devise a method of rewarding desired behaviors and punishing negative behavior. This method assigns positive values to the desired actions to encourage the agent and negative values to undesired behaviors.This programs the agent to seek long-term and maximum overall reward to achieve an optimal solution.

These long-term goals help prevent the agent from stalling on lesser goals. With time, the agent learns to avoid the negative and seek the positive. This learning method has been adopted in artificial intelligence (AI) as a way of directing unsupervised machine learning through rewards and penalties.


Gaming is likely the most common usage field for reinforcement learning. It is capable of achieving superhuman performance in numerous games. A common example involves the game Pac-Man.

In robotics, reinforcement learning has found its way into limited tests.Itis also used in operations research, information theory, game theory, control theory, simulation-based optimization, multiagent systems, swarm intelligence, statistics, and genetic algorithms.


Reinforcement learning, while high in potential, can be difficult to deploy and remains limited in its application. One of the barriers to the deployment of this type of machine learning is its reliance on an exploration of the environment.

For example, if you were to deploy a robot that was reliant on reinforcement learning to navigate a complex physical environment, it will seek new states and take different actions as it moves. It is difficult to consistently take the best actions in a real-world environment, however, because of how frequently the environment changes.

The time required to ensure the learning is done properly through this method can limit its usefulness and be intensive on computing resources. As the training environment grows more complex, so too do demands on time and compute resources.

Supervised learning can deliver faster, more efficient results than reinforcement learning to companies if the proper amount of data is available, as it can be employed with fewer resources.


Rather than referring to a specific algorithm, the field of reinforcement learning is made up of several algorithms that take somewhat different approaches. The differences are mainly due to their strategies for exploring their environments.

  1. State-action-reward-state-action (SARSA): –

This reinforcement learning algorithm starts by giving the agent what’s known as a policy. The policy is essentially a probability that tells it the odds of certain actions resulting in rewards, or beneficial states.

  1. Q-learning: – This approach to reinforcement learning takes the opposite approach. The agent receives no policy, meaning its exploration of its environment is more self-directed.
  2. Deep Q-Networks: – These algorithms utilize neural networks in addition to reinforcement learning techniques. They utilize the self-directed environment exploration of reinforcement learning. Future actions are based on a random sample of past beneficial actions learned by the neural network.


  1. Supervised learning: – In supervised learning, algorithms train on a body of labeled data. Supervised learning algorithms can only learn attributes that are specified in the data set. Common applications of supervised learning are image recognition models. These models receive a set of labeled images and learn to distinguish common attributes of predefined forms.
  2. Unsupervised learning: – In unsupervised learning, developers turn algorithms loose on fully unlabelled data. The algorithm learns by cataloging its observations about data features without being told what to look for.

Semi-supervised learning: –This method takes a middle-ground approach. Developers enter a relatively small set of labeled training data, as well as a larger.

  1. corpus of unlabelled data. The algorithm is then instructed to extrapolate what it learns from the labeled data to the unlabelled data and draw conclusions from the set as a whole.
  2. Reinforcement learning: – This takes a different approach altogether. It situates an agent in an environment with clear parameters defining beneficial activity and nonbeneficial activity and an overarching endgame to reach. It is similar in some ways to supervised learning in that developers must give algorithms specified goals and define rewards and punishments.


Two kinds of reinforcement learning methods are:

  1. Positive:

It is defined as an event, that occurs because of specific behavior. It increases the strength and the frequency of the behavior and impacts positively on the action taken by the agent.

This type of Reinforcement helps you to maximize performance and sustain change for a more extended period. However, too much Reinforcement may lead to over-optimization of the state, which can affect the results

  1. Negative:

Negative Reinforcement is defined as the strengthening of behavior that occurs because of a negative condition that should have been stopped or avoided. It helps you to define the minimum stand of performance. However, the drawback of this method is that it provides enough to meet up the minimum behavior.


Here are prime reasons for using Reinforcement Learning:

  1. It helps you to find which situation needs an action
  2. Helps you to discover which action yields the highest reward over a longer period.


  1. You can’t apply the reinforcement learning model in all the situation. Here are some conditions when you should not use the reinforcement learning model.
  2. When you have enough data to solve the problem with a supervised learning method

CONCLUSION Reinforcement Learning addresses the problem of learning control strategies for autonomous agents with least or no data. RL algorithms are powerful in machine learning as collecting and labeling a large set of sample patterns costs more than data itself. The key distinguishing factor of reinforcement learning is how the agent is trained. Instead of inspecting the data provided, the model interacts with the environment, seeking ways to maximize the reward. In the case of deep reinforcement learning, a neural network is in charge of storing the experiences and thus improves the way the task is performed.

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