The following are the basic terminologies associated with reinforcement learning:
- Agent: This we create by programming such that it is able to sense the environment, perform actions, receive feedback, and try to maximize rewards.
- Environment: The world where the agent resides. It can be real or simulated.
- State: The perception or configuration of the environment that the agent senses. State spaces can be finite or infinite.
- Rewards: Feedback the agent receives after any action it has taken. The goal of the agent is to maximize the overall reward, that is, the immediate and the future reward. Rewards are defined in advance. Therefore, they must be created properly to achieve the goal efficiently.
- Actions ...