What is reinforcement learning and how does it work?
Reinforcement learning is a subfield of machine learning in which an agent learns to make optimal decisions in an environment through rewards and penalties. It tries different actions and gradually improves its behaviour to achieve the greatest possible long-term benefit.
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What is reinforcement learning?
Put simply, reinforcement learning refers to learning through reinforcement. It is a method within the field of machine learning. Alongside supervised learning and unsupervised learning, it represents the third major approach to training algorithms and agents to make decisions autonomously. The primary goal is to develop intelligent solutions for complex control and decision-making problems.
With this approach to machine learning, unlike supervised and unsupervised learning, no data is required for conditioning. Instead, the data is generated during training using a trial-and-error method and labelled at the same time. The program runs numerous training iterations within a simulation environment to deliver a precise result. In other words, only signals are provided to support the system.
The goal of this training approach is for artificial intelligence to autonomously solve highly complex control problems without relying on prior human knowledge. Compared to conventional engineering methods, this makes development faster and more efficient and, ideally, leads to optimal solutions.
How does reinforcement learning work?
Reinforcement learning describes a range of methods in which an algorithm or software agent learns strategies autonomously. The objective is to maximise rewards within a simulated environment. The computer performs an action and then receives feedback. The software agent is given no prior information about which actions are most promising and must determine its approach independently through a trial-and-error process.
To improve the effectiveness of the process, the computer receives rewards at different points in time, which influence its strategies. Through these signals, the software agent learns to assess the consequences of specific actions within the simulated environment.
To train a reinforcement learning system effectively, Q-learning is often used. The Q-function represents the expected future reward of taking a specific action in a given state. The goal of reinforcement learning is to use these estimates to develop an optimal policy for decision-making.
Traditionally, Q-learning represents the policy in a Q-table, where states and actions are listed explicitly and each combination stores a value for the expected reward. However, this approach is only practical in highly simplified environments. In modern scenarios with large or continuous state and action spaces, the Q-table is replaced by function approximation methods, most commonly using neural networks.
Where and when is reinforcement learning used?
Reinforcement learning is used in many different fields where machines or systems are expected to make decisions autonomously and learn from experience. The goal is always to develop better strategies through continuous learning and to optimise processes. Key application areas include:
- Robotics: In robotics, reinforcement learning helps robots learn complex movement sequences such as grasping, walking, or navigating. Instead of programming every movement manually, robots learn through trial and error how to perform tasks efficiently. This also enables them to adapt to new environments or situations.
- Game development and AI training: Reinforcement learning became widely known through its successes in games such as chess, Go, and video games. Artificial intelligence systems run millions of simulations to learn optimal strategies and, in some cases, outperform human players.
- Finance: In the financial sector, this learning approach is used to optimise trading strategies or manage portfolios automatically. The algorithm learns how to respond to market changes and balance risk and return, enabling better long-term investment decisions.
- Control of complex systems: Another application of reinforcement learning is the control of complex systems, such as intelligent traffic management systems. It is also used in quality control, smart power grids, supply chain optimisation in logistics companies, and factory automation.
- Healthcare and energy optimisation: In healthcare, reinforcement learning supports personalised treatments by recommending optimal therapy plans. In energy management, it helps dynamically control energy consumption and distribution to conserve resources and reduce costs.
A range of libraries is available to simplify the development of reinforcement learning algorithms. For instance, the AI research company DeepMind provides Acme, a dedicated Python library. In addition, Stable-Baselines3 offers a wide selection of ready-to-use implementations for well-known algorithms.