Definition
Reinforcement Learning (RL) is a machine learning approach that deals with how agents can learn to make decisions through trial and error to increase total rewards. RL enables machines to learn by interacting with an environment and receiving feedback based on their actions in the form of rewards or penalties [1].
A good example of reinforcement learning would be a bank call center queue management system with the goal of reducing customer wait times. The AI learns through trial and error without human judgement to route calls efficiently during peak banking hours and gets the reward of shorter queues and higher customer satisfaction scores.
How
Reinforcement learning works [2].Origin
Reinforcement
learning originated in the 1930’s through the pioneering work of behavioral
psychologist B. F. Skinner. He found that reinforcement could be used to shape
animal behavior when he demonstrated that animals could be trained to perform
complex tasks through simple reinforcement mechanisms, such as receiving a food
reward for performing a desired control. This led to him developing the concept
of positive reinforcement based on the idea that an animal or any agent can
learn to optimize its behavior by learning from past experience.
Today, RL is viewed as a type of machine learning that enables computers to learn from types of reinforcement, such as punishments or rewards, in order to improve [3]
Context and
Usage
Reinforcement
learning (RL) can be applied to a number of real-world use cases, some of which
include the following:
Training system
which would issue custom instructions and materials with respect to the
requirements of students
Text
summarization engines, dialogue agents (text, speech), gameplays
Robotics for
Industrial Automation
Autonomous Self
Driving Cars
Why it Matters
Reinforcement learning is an important approach developers use to train machine learning systems due to the fact that it enables an agent to learn to navigate the complexities of the environment for which it was created. For instance, a robot in an industrial setting can be taught to perform a specific task or an agent can be taught to control a video game. With time, the agent learns from its environment and optimizes its behaviors using a feedback system that involves rewards and punishments [4].
In Practice
A good real-life case study of reinforcement learning in practice can be seen in the case of AlphaGo Zero. Using reinforcement learning, AlphaGo Zero was able to learn the game of Go from scratch by playing against itself. After 40 days of self-training, Alpha Go Zero was able to outperform the version of Alpha Go known as Master that has defeated world number one Ke Jie [5].
See Also
Related Learning
Approaches:
- Reinforcement Learning from Human Feedback (RLHF): Training method that uses human preferences to guide reinforcement learning
- Similarity Learning: Machine learning approach that teaches models to measure similarity between objects
- Singularity: Hypothetical point when AI surpasses human intelligence across all domains
- Strong AI: Theoretical AI with human-level general intelligence across all domains
- Supervised Learning: Learning from labeled data with clear input-output mappings
References
- Geeksforgeeks. (2025). Reinforcement Learning.
- AWS. (2025). What is Reinforcement Learning?
- Bowyer, C., M. (2022). A Crude History of Reinforcement Learning (RL)
- Gillis, A., S., Carew, J., M. (2024). What is reinforcement learning?
- Mwiti, D. (2025). 10 Real-Life Applications of Reinforcement Learning.