Which component encodes the agent's plan of action, mapping states to actions?

Which component encodes the agent's plan of action, mapping states to actions?

• A. The environment
• B. The reward signal
• C. The value function
• D. The policy

Answer: (D)

In reinforcement learning, the policy encodes the agent's plan of action by specifying which action to take in each possible state. It can be deterministic, where a given state leads to a single action, or stochastic, where a state leads to a distribution over actions. This is distinct from the environment, which is the world the agent interacts with; the reward signal, which tells the agent how good a particular outcome is; and the value function, which estimates how good it is to be in a state or to take a certain action in a state by forecasting future cumulative rewards. The policy is the decision rule that directly drives behavior, determining the agent’s actions based on the current state, while the value function and rewards provide evaluation and learning signals, and the environment provides the outcomes.

In reinforcement learning, the policy encodes the agent's plan of action by specifying which action to take in each possible state. It can be deterministic, where a given state leads to a single action, or stochastic, where a state leads to a distribution over actions. This is distinct from the environment, which is the world the agent interacts with; the reward signal, which tells the agent how good a particular outcome is; and the value function, which estimates how good it is to be in a state or to take a certain action in a state by forecasting future cumulative rewards. The policy is the decision rule that directly drives behavior, determining the agent’s actions based on the current state, while the value function and rewards provide evaluation and learning signals, and the environment provides the outcomes.