Reinforcement Learning
April 4, 2025
With the world of AI constantly evolving, new techniques and ideas are widely pursued in the hopes of hitting the AI “gold mine.” From simple machine learning models involving linear regression, to neural networks, to more recent reinforcement learning, as computing power has improved, so have machine learning methods.
However, reinforcement learning has existed since the beginning of machine learning’s inception. In the 1950s and 1960s, Richard Bellman developed the founding principles of reinforcement learning, which allowed for robotics and control problems to be solved in a theoretical sense. However, the technology of the time severely limited the famous “Bellman’s equations” from being applied in a practical way. They stayed as theoretical principles for much longer, until recently, when the same computing power that allowed AI to explode also gave rise to practical applications of reinforcement learning.
The important question at this point becomes: what is reinforcement learning? In reinforcement learning, the computer, referred to as the “agent,” is meant to be completing a task in an optimized manner. Another machine learning method, supervised learning, feeds data to the computer model to predict, compares it to the pre-defined “ground-truth” labels, and attempts to minimize the error over several iterations of this process. When data with labels cannot be found, one may often resort to unsupervised learning, a process where data is grouped together through shared characteristics, thus predicting which group new data will fall under. However, if a computer model is meant to optimally play a video game such as chess, or even more complex examples like StarCraft, there is no simple way for a computer to solve such a task. This is where reinforcement learning comes into play. Reinforcement learning has the agent repeat the task over and over again, for hundreds of thousands or even millions of iterations until the desired performance level is reached.
In broad terms, the idea of reinforcement learning seems simple. However, its implementation is significantly more complex. For the model to be able to optimally solve the task, it must find the optimal policy. The agent’s policy is what guides the agent’s decision-making in the task, allowing the task to be “solved” successfully. In a maze, this may mean the direction the agent should go at any designated square. In chess, this may mean the move to make in a certain position. The agent “learns” this policy throughout the training process, a process that must be tuned and optimized to the specific task by the programmer.
Reinforcement learning, while old in its origins, is shaking up the AI world with the new path for innovation that it has created. Self-driving cars, the world of the future, are currently being trained with reinforcement learning. There is a bright future ahead in the world of AI.