An RL agent is an optimization process that learns from experience, using data from its environment that it has collected through its own observations. It starts out knowing nothing about a task explicitly, learns by trial and error about what happens when it makes decisions, keeps track of successful decisions, and makes those same decisions under the same circumstances in the future.
In fields other than AI, RL is also referred to as dynamic programming. It takes much of its basic operating structure from behavioral psychology, and many of its mathematical constructs such as utility functions are taken from fields such as economics and game theory.
Let's get familiar with some key concepts in RL:
- Agent: This is the decision-making entity.
- Environment: This is the world in which the agent operates, such as a game to win or task to accomplish.
- State: This...
