Reinforcement Learning (RL) is a machine learning technique that allows a model to learn by trial and error in an interactive environment. It is simply the science of decision-making.
In this article, you will learn how RL works, its benefits, and its challenges.
How Reinforcement Learning Works
Reinforcement learning involves a model exploring an unknown interactive environment for a goal. The model must learn to perturb and sense the condition of the environment by its actions to achieve maximal reward.
The main elements of a reinforcement learning system include the learner or model, the environment explored by the model, the policy used by the model to take action, and the reward system that the model follows upon taking action.
There are two types of RL methods, positive and negative reinforcement. The goal of positive reinforcement is to amplify desired behavior through the introduction of a favorable stimulus immediately following the occurrence of the target behavior.
On the other hand, negative reinforcement involves promoting specific behaviors by eliminating or avoiding a negative condition or stimulus.
Benefits of Reinforcement Learning
Reinforcement learning is used for a variety of complex problems which other machine learning algorithms cannot handle. Below are some of the benefits of RL.
Works in uncertain and dynamic environments
Reinforcement learning algorithms are designed to react to changes in an environment. This allows them to work in dynamic environments.
RL doesn’t require a separate data collection step
In training reinforcement learning, data is collected through a direct interaction between the model and the environment. This leads to a reduction of burden on the supervisor that is in control of the training process.
Tackles the problem as a whole
Unlike conventional ML algorithms that are designed to excel at specific subtasks without focusing on the big picture, RL doesn’t divide a problem into subtasks. It tackles the problem fully.
Challenges with Reinforcement Learning
Even though RL algorithms have been very successful in tackling complex problems in simulated environments, their real-world adoption has been a bit slow. Below are some of the challenges with RL.
Lack of interpretability
An RL model takes actions based on its experience after it has learned the optimal policy and has been deployed in an environment. However, the reason for these actions may be unclear to an external observer. This interferes with the development of trust between the observer and the model.
Reinforcement learning model needs extensive experience
Before a good solution can be found in complex environments with high-dimensional state spaces, extensive exploration is required.
Examples of Reinforcement Learning
RL is one of the most popular topics in the world of artificial intelligence. Here are a couple of ways RL can be used:
- Natural Language Processing: This covers anything from question answering, to predictive text, and text summarization.
- Automated Robots: Many devices like robot waiters, and robotic factory workers use RL to become better and more accurate at their jobs.
- Image Processing: RL agents can be used for a number of image-processing tasks such as CCTV analysis, image-to-text interpretation and so much more.
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