Reinforcement learning is a subfield of machine learning that focuses on how an agent can learn to take actions in an environment in order to maximize a reward signal. In reinforcement learning, the agent is not given explicit instructions on how to solve the problem, but instead must learn by trial-and-error through interaction with the environment.

The reinforcement learning process typically involves the following components:

1. Agent: The decision-making entity that interacts with the environment and takes actions.
2. Environment: The external world that the agent interacts with and receives feedback from.
3. State: The current situation or condition of the environment.
4. Action: The decision or choice made by the agent to influence the environment.
5. Reward: The feedback signal that the agent receives from the environment after taking an action.

The goal of the reinforcement learning agent is to learn a policy, or a set of rules, that will maximize the cumulative reward over time. The agent achieves this by exploring the environment and learning from its experiences.

Reinforcement learning has been successfully applied to a wide range of problems, including game playing, robotics, and autonomous driving. It is particularly useful in situations where explicit instructions or rules are difficult to define or are not available.