Reinforcement learning is a type of machine learning that focuses on training an agent to take actions in an environment in order to maximize a reward signal. The goal of the agent is to learn a policy, which is a mapping from states to actions, that maximizes its expected cumulative reward over time.

The RL problem can be thought of as a Markov decision process (MDP), which consists of a set of states, a set of actions, a transition function that defines the probability of moving from one state to another after taking an action, a reward function that defines the reward received for taking an action in a particular state, and a discount factor that determines how much future rewards are valued relative to immediate rewards.

The agent interacts with the environment by taking actions and observing the resulting state and reward. Based on this feedback, the agent updates its policy to improve its expected cumulative reward over time.

There are several different types of RL algorithms, including value-based methods, policy-based methods, and actor-critic methods. Value-based methods learn a value function that estimates the expected cumulative reward of following a particular policy, while policy-based methods learn a policy directly. Actor-critic methods combine elements of both value-based and policy-based methods, by learning both a value function and a policy simultaneously.

RL has many applications in areas such as robotics, gaming, and recommendation systems. For example, RL can be used to train a robot to navigate a maze or learn to perform a task, such as grasping an object. In gaming, RL can be used to train game-playing agents that can learn to play games at superhuman levels. In recommendation systems, RL can be used to learn a personalized recommendation policy for each user, based on their interactions with the system.

Overall, RL is a powerful tool for solving complex problems, and has the potential to revolutionize many areas of research and industry.