Reinforcement Learning: What It Is, Algorithms, Types and Examples

In reinforcement learning, an agent is an entity that interacts with its environment to achieve a specific goal.

The actions of a reinforcement learning agent have a direct impact on the environment. In the case of playing chess, the board serves as the environment where the agent's current state and action are received and processed to provide a reward and a new state.

In reinforcement learning, the environment provides feedback that determines the validity of the agent's actions in each state. This feedback is crucial to allowing the machine to learn independently as the reward it receives is the only means of critique to guide its learning process.