Penalty-Based Trust Region Implementation
A common method for implementing a trust region is to modify the objective function by adding a penalty term. This approach constrains the size of the policy update by penalizing significant deviations from a reference policy. The penalty is calculated using a divergence measure that quantifies the difference between the current policy and the reference, thereby discouraging updates that would move the policy outside of the trusted area.
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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
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Penalty-Based Trust Region Implementation
Trust Region Policy Optimization
An engineer is training a reinforcement learning agent using a policy-based method. They observe the following training behavior: the agent's performance steadily improves for several iterations, but then suddenly collapses, becoming significantly worse than before. This pattern of gradual improvement followed by a catastrophic drop in performance repeats. Which of the following statements provides the most likely explanation for this unstable training dynamic?
Stabilizing Policy Updates in Reinforcement Learning
The Trust Region Size Trade-off
Learn After
Log-Probability Difference as a Policy Divergence Penalty
An engineer is training a policy model and wants to prevent large, destabilizing updates between training iterations. They modify their original objective function,
J(θ), to a new objective function,J_new(θ) = J(θ) - β * D(θ, θ_old), whereθrepresents the current policy parameters,θ_oldrepresents the parameters from the previous iteration,Dis a function that measures the divergence between the two sets of parameters (a larger value means more divergence), andβis a positive coefficient. During optimization, the goal is to maximizeJ_new(θ). What is the primary effect of the- β * D(θ, θ_old)term on the training process?Stabilizing Reinforcement Learning Training
Choosing an Objective Function for Stable Policy Updates
Stabilizing Policy Updates with a Divergence Penalty
When implementing a penalty-based trust region for policy optimization where the goal is to maximize the objective function, increasing the weight of the penalty term will expand the trusted area, allowing the policy to make larger updates.