Reinforcement learning
Reinforcement learning is a machine learning method in which an agent learns by trial and error. It receives rewards or penalties for its actions and optimises its behaviour over time so the total reward is as high as possible. The method is used in robotics, control systems and games among other things. The training method RLHF builds on it too.
Also known as: reinforcement learning, RL
How does reinforcement learning work?
At the centre of reinforcement learning is an agent interacting with an environment. The agent takes an action, the environment's state changes and it receives feedback as a reward or penalty. The aim is to develop a strategy maximising the cumulative reward across many steps.
Unlike supervised learning there are no given correct answers. The agent has to find out for itself, through repeated trial, which actions pay off in which situations. This process of trial and error makes reinforcement learning particularly flexible but also compute-intensive.
Reward, exploration and strategy
The reward signal is at the heart of reinforcement learning. It defines which behaviour is wanted and steers the whole learning process. Designing that signal thoughtfully is decisive, since a badly defined reward system can lead to unwanted or nonsensical behaviour.
One central challenge is the balance between exploration and exploitation. The agent has to try new actions to discover better strategies and at the same time use proven actions to secure rewards. The right balance between exploring and exploiting largely determines how well it learns.
Typical areas of use
Reinforcement learning shows its strengths wherever decisions have to be made across several steps in complex, dynamic environments. In robotics, machines learn to grasp or walk this way. In control engineering, agents optimise processes such as regulating energy or traffic systems.
The method became particularly well known through games. AI systems reached superhuman level at board and video games by refining their strategies over millions of matches. These successes demonstrate impressively how capable reinforcement learning is in clearly defined environments.
Reinforcement learning and RLHF
A particularly significant development is Reinforcement learning from human feedback, RLHF for short. Human feedback flows into the reward signal to steer a model’s behaviour deliberately. This method plays a central role in training modern large language models.
Through RLHF, language models learn to give helpful, polite, safe answers matching users' expectations. Reinforcement learning thus forms an important bridge between purely data-driven training and aligning AI systems to human values and preferences.
Using reinforcement learning in a company
In practice reinforcement learning makes high demands on computing power, data volume and the design of the environment. It suits optimisation and control tasks in particular, where clear objectives can be defined and there is enough opportunity to experiment.
At Elisabit we help companies judge the potential of modern AI methods realistically and build suitable solutions. Whether classic machine learning or advanced methods such as reinforcement learning — we help you choose the right technology for your goals and put it to profitable use.
Frequently asked questions
What is the difference from supervised learning?
When Supervised learning a model learns from labelled examples with known correct answers. In reinforcement learning there are no such given answers; instead an agent learns from its own experience through rewards and penalties. Reinforcement learning suits sequential decision problems in dynamic environments.
What do exploration and exploitation mean?
Exploration means trying new actions to discover better strategies. Exploitation means using actions already known to succeed, to secure reliable rewards. A successful agent has to balance both to act optimally in the long run.
What is RLHF?
RLHF stands for reinforcement learning from human feedback. Human feedback is used to shape the reward signal and align a model's behaviour deliberately. This method is an important building block in training modern large language models and produces helpful, safe answers.
Where is reinforcement learning used?
Reinforcement learning is used in robotics, process control and games. It suits wherever a system can learn to make optimal decisions through repeated trial in an environment. It also plays an important part in training AI models through RLHF.
Related terms
Machine learning enables systems to learn from data and make predictions without being explicitly programmed.
Supervised learning is machine learning with labelled training data, for classification and regression.
Unsupervised learning finds structures and patterns in data on its own, without given answers.
RLHF is a training method that uses human feedback to make model responses more helpful and safer.
Deep learning uses deep neural networks to recognise complex patterns in large volumes of data automatically.
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