Reinforcement learning is an artificial intelligence approach that emphasizes the learning of the system through its interactions with the environment. With reinforcement learning, the system adapts its parameters based on feedback received from the environment, which then provides feedback on the decisions made. For example, a system that models a chess player who uses the result of the preceding steps to improve their performance is a system that learns with reinforcement. Current research on learning with reinforcement is highly interdisciplinary, and includes researchers specializing in genetic algorithms, neural networks, psychology, and control engineering.

The following figure summarizes the three types of learning, with the related problems to address: