Reference Policy ($\pi_{\theta_{\text{ref}}}$)

Policy Probability Ratio (Ratio Function)

An autonomous agent is being trained to navigate a maze. The agent's decision-making process at any given intersection (a 'state') is determined by a specific component of its programming. Which of the following scenarios best exemplifies this decision-making component?

An autonomous agent is programmed to navigate a grid. When it reaches a specific grid cell (state 'S'), it must choose an action. Consider two different versions of the agent's programming:

• Agent 1: When in state 'S', it is programmed to always choose the action 'move North'.
• Agent 2: When in state 'S', it is programmed to choose 'move North' with 70% probability and 'move East' with 30% probability.

Which statement best analyzes the difference in how these two agents map states to actions?

An agent's goal is to navigate a simple environment and maximize its total reward. The agent is currently in a state 'S'. From this state, it can take one of two actions: 'Action 1' which consistently leads to a reward of +10, or 'Action 2' which consistently leads to a reward of -5. Consider two possible behavior patterns for the agent when it is in state 'S':

• Behavior A: The agent chooses 'Action 1' with a 100% probability.
• Behavior B: The agent chooses 'Action 1' with a 50% probability and 'Action 2' with a 50% probability.

Which behavior pattern is superior for achieving the agent's goal, and why?