An autonomous agent is being trained to navigate a maze. At a specific intersection (a 'state'), it can either 'turn left' or 'turn right' (the 'actions'). We compare the agent's current decision-making strategy to its initial, less-developed strategy. For the action 'turn left' at this intersection, the ratio of its probability under the current strategy to its probability under the initial strategy is 2.5. What is the most accurate interpretation of this value?

Analyzing Policy Updates in a Game-Playing AI

An AI agent is being trained to play a video game. The training process aims to increase the likelihood that the agent performs a specific beneficial action, 'use health potion', when its health is low. After a successful training update that achieves this goal, the ratio of the probability of 'use health potion' under the new policy to its probability under the old policy will be less than 1.