Reinforcement learning (RL) is an ML method that is neither supervised learning nor unsupervised learning. In this method, a reward definition is provided as input to this kind of a learning algorithm at the start. As the algorithm is not provided with labeled data for training, this type of learning algorithm cannot be categorized as supervised learning. On the other hand, it is not categorized as unsupervised learning, as the algorithm is fed with information on reward definition that guides the algorithm through taking the steps to solve the problem at hand.

Reinforcement learning aims to improve the strategies used to solve any problem continuously by relying on the feedback received. The goal is to maximize the rewards while taking steps to solve the problem. The rewards obtained are computed by the algorithm itself going by the rewards and penalty definitions. The idea is to achieve optimal steps that maximize the rewards to solve the problem at hand.

The following diagram is an illustration depicting a robot automatically determining the ideal behavior through a reinforcement learning method within the specific context of fire:

A machine outplaying humans in an Atari video game is termed as one of the foremost success stories of reinforcement learning. To achieve this feat, a large number of example games played by humans are fed as input to the algorithm that learned the steps to take to maximize the reward. The reward in this case is the final score. The algorithm, post learning from the example inputs, just simulated the pattern at each step of the game that eventually maximized the score obtained.

Though it might appear that reinforcement learning can be applied to game scenarios only, there are numerous use cases for this method in industry as well. The following examples mentioned are three such use cases:

• Dynamic pricing of goods and services based on spontaneous supply and demand targeted at achieving profit maximization is achieved through a variant of reinforcement learning called Q-learning.
• Effective use of space in warehouses is a key challenge faced by inventory management professionals. Market demand fluctuations, the large availability of inventory stocks, and delays in refilling the inventory are the key constraints that affect space utilization. Reinforcement learning algorithms are used to optimize the time to procure inventory as well as to reduce the time to retrieve the goods from warehouses, thereby directly impacting the space management issue referred to as a problem in the inventory management area.
• Prolonged treatments and differential drug administration is required in medical science to treat diseases such as cancer. The treatments are highly personalized, based on the characteristics of the patient. Treatment often involves variations of the treatment strategy at various stages. This kind of treatment plan is typically referred to as a dynamic treatment regime (DTR). Reinforcement learning helps with processing the clinical trials data to come up with the appropriate personalized DTR for the patient, based on the characteristics of the patient that are fed in as inputs to the reinforcement learning algorithm.

There are four very popular reinforcement learning algorithms, namely Q-learning, state-action-reward-state-action (SARSA), deep Q network (DQN), and deep deterministic policy gradient (DDPG).