RL glossary
We have learned several important and fundamental concepts of RL. In this section, we revisit several important terms that are very useful for understanding the upcoming chapters.
Agent: The agent is the software program that learns to make intelligent decisions, such as a software program that plays chess intelligently.
Environment: The environment is the world of the agent. If we continue with the chess example, a chessboard is the environment where the agent plays chess.
State: A state is a position or a moment in the environment that the agent can be in. For example, all the positions on the chessboard are called states.
Action: The agent interacts with the environment by performing an action and moves from one state to another, for example, moves made by chessmen are actions.
Reward: A reward is a numerical value that the agent receives based on its action. Consider a reward as a point. For instance, an agent receives +1 point (reward) for a good...