Importance Sampling for Utility Estimation in Policy Gradients
Importance sampling is a technique used to improve policy gradient methods in reinforcement learning. It works by refining the estimation of the utility function, , to account for differences between the current policy being optimized and the reference policy used to collect the trajectory data. This adjustment helps in obtaining more reliable and stable policy updates during training.
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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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Policy Gradient Objective with Importance Sampling
An agent is being trained using a policy gradient method. After each update to its decision-making process (the policy), the experiences (trajectories) it previously collected are no longer perfectly representative of its new behavior. This mismatch can lead to inaccurate estimates of the value of those past trajectories, causing instability in the training process. Which of the following approaches directly addresses this issue by adjusting the value calculation to account for the change in the policy?
Evaluating Training Strategies for a Robotic Arm
Addressing Data Mismatch in Policy Gradient Training