ABSTRACT
Reinforcement learning fills the gap between supervised learning, where the algorithm is trained on the correct answers given in the target data, and unsupervised learning, where the algorithm can only exploit similarities in the data to cluster it. Reinforcement learning is usually described in terms of the interaction between some agent and its environment. The importance of reinforcement learning for psychological learning theory comes from the concept of trial-and-error learning, which has been around for a long time, and is known as Law of Effect. Reinforcement learning maps states or situations to actions in order to maximise some numerical reward. Reinforcement learning has been used successfully for many problems, and results of computer modelling of reinforcement learning have been of great interest to psychologists, as well as computer scientists, because of the close links to biological learning. Reinforcement learning has been used in other robotic applications, including robots learning to follow each other, travel towards bright lights, and even navigate.
