What is reinforcement learning?

Reinforcement learning is a type of machine learning where agents learn to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. This approach mimics how humans and animals learn from their actions: when they perform well, they receive positive reinforcement (rewards) that encourages repetition of that behavior, and when they perform poorly, they receive negative feedback (penalties) that discourages that behavior.

In this context, agents explore their environment, take actions, and learn optimal strategies over time to maximize cumulative rewards. This distinguishes reinforcement learning from other learning paradigms such as supervised learning, where models learn from labeled datasets, or unsupervised learning, which does not utilize labels at all. The focus here is on learning through experience, making it particularly effective for scenarios where the correct actions are not known in advance and must be discovered through trial and error.

Understanding reinforcement learning is essential for various applications, including robotics, game playing, and autonomous systems, where the ability to learn and adapt in dynamic environments is crucial.