Explain core components of reinforcement learning
Company: Amazon
Role: Applied Scientist
Category: Machine Learning
Difficulty: medium
Interview Round: Technical Screen
In reinforcement learning, we model an agent that interacts with an environment over time. The agent observes the state of the environment, takes actions, and receives rewards.
Describe the standard components of a reinforcement learning formulation:
- What are the basic elements of the underlying Markov decision process?
- What additional agent-side components are commonly defined?
- How do these components interact over time in the RL loop?
Give clear definitions and briefly explain the role of each component.
Overview: This question evaluates understanding of reinforcement learning fundamentals, specifically the components of Markov decision processes, typical agent-side elements, and their temporal interaction in the agent-environment loop.
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