Reinforcement learning is one of the three basic Machine Learning paradigms, alongside Supervised ML and Unsupervised ML.
While supervised learning and unsupervised learning algorithms attempt to discover patterns in labeled and unlabeled data, reinforcement learning involves training an agent through interactions with its environment.
To learn to maximize rewards from these interactions, the agent makes decisions between trying new actions to learn more about the environment (exploration), or using current knowledge of the environment to take the best action (exploitation).
The purpose of reinforcement learning is for the agent to learn an optimal (or near-optimal) policy that maximizes the reward function or other user-provided reinforcement signal that accumulates from immediate rewards.
A basic reinforcement learning agent interacts with its environment in discrete time steps. At each time step t, the agent receives the current state and reward . It then chooses an action from the set of available actions, which is subsequently sent to the environment. The environment moves to a new state and the reward associated with the transition is determined. The goal of a reinforcement learning agent is to learn a policy:
that maximizes the expected cumulative reward.
