Reinforcement Learning (RL)
Reinforcement Learning is a core area of machine learning that focuses on training software agents to make decisions within an environment to achieve a goal or maximize cumulative rewards over time. Unlike supervised learning, which relies on labeled data, or unsupervised learning, which looks for hidden patterns in data without explicit labels, RL involves an agent that interacts with its environment and learns from the consequences of its actions through rewards or penalties.
The agent's objective is to learn a policy — a strategy for choosing actions based on the current state — that maximizes the long-term reward. This learning process involves a delicate balance between exploring new actions to discover potentially better rewards and exploiting known actions that already yield good rewards, a dilemma known as the exploration-exploitation trade-off.
A classic example of reinforcement learning is training an agent to play board games or video games where the rules of the environment are known but the optimal strategy is not. The agent learns by playing the game multiple times, receiving rewards for winning moves or sequences of moves, and penalties for losing or making poor decisions. Over time, the agent identifies which actions lead to higher rewards in different states of the game, effectively learning strategies that increase its chances of winning.
Another practical application is in robotics, where RL can be used to teach robots to perform tasks like walking or picking up objects. The robot learns through trial and error, adjusting its actions based on feedback from its environment to improve its performance at the task. In autonomous driving, RL can be used to develop control policies for vehicles, enabling them to make complex sequences of decisions in real-time, such as when to accelerate, brake, or steer, to navigate safely and efficiently.