Explain core components of reinforcement learning

Reinforcement learning involves modeling an agent that interacts with an environment across time. The agent perceives the environment's state, selects actions, and obtains rewards. Outline the standard components of a reinforcement learning setup:

• What are the fundamental elements of the underlying Markov decision process?
• Which additional components on the agent side are typically defined?
• How do these components interact over time within the RL loop? Provide clear definitions and briefly describe the role of each component. Overview: This question assesses knowledge of reinforcement learning basics, particularly the components of Markov decision processes, common agent-side elements, and how they interact over time in the agent-environment loop. Read the full Amazon Applied Scientist interview experience that this question was taken from.