What is the Markov Decision Process?
Instruction: Provide a simple explanation of the Markov Decision Process in the context of Reinforcement Learning.
Updated
Example Answer
A Markov decision process describes sequential decisions using states, available actions, a transition model, and rewards. The objective also specifies a horizon or discount factor. A policy tells the agent how to choose actions in each state.
The key assumption is the Markov property: once the current state and action are known, the distribution of the next state and reward does not depend on earlier history. That is stronger than calling a state a snapshot of the environment. The state must retain the information needed to predict what happens next.
For a warehouse robot, position alone may be insufficient. Two robots at the same position but with different battery levels or carried loads can have different feasible actions and outcomes. I would include those variables, and time remaining if the task has a fixed deadline. If the agent observes only part of the relevant state, I would treat it as a partially observable problem and consider history or a belief state.
Follow-up
To check a proposed state representation, ask whether two histories ending in the same representation imply the same outcome distribution for the same action.