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Choosing an Objective Function for Stable Policy Updates
An AI engineer is training a reinforcement learning agent. They are considering two different objective functions to maximize for updating the agent's policy parameters from θ_old to θ_new:
- Objective A:
J(θ_new) - Objective B:
J(θ_new) - α * D(θ_new, θ_old)
Here, J(θ) is the standard performance measure, D is a function that calculates the difference between the new and old policy parameters (a larger value means a bigger change), and α is a positive constant.
Explain why Objective B is generally preferred for ensuring stable and reliable training progress. In your explanation, describe the role of the term - α * D(θ_new, θ_old).
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Choosing an Objective Function for Stable Policy Updates
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