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Interpreting Policy Changes
An agent's decision-making process is being updated. For a specific situation, the probability of taking a certain action under the old (reference) process was 0.5. Under the new (current) process, the probability of taking the same action is 0.25.
- Calculate the ratio of the current probability to the reference probability.
- Explain what this resulting ratio signifies about the change in the agent's behavior for this specific action.
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Application in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
Related
Increased Action Probability Condition
Policy Probability Ratio Less Than One
Bound Function for Policy Probability Ratio
Policy Probability Ratio Greater Than One
Upper-Bound Clipping Function for Policy Ratios
Evaluating a Policy Change
In an off-policy reinforcement learning scenario, an agent is in a specific state. The policy that originally collected the training data (the reference policy) selected a particular action with a probability of 0.2. The agent's current, updated policy would select that same action with a probability of 0.8. What does the resulting probability ratio imply about how the reward for this action-state pair should be treated during the policy update?
Interpreting Policy Changes