Baseline's Impact on Reward Variance vs. Gradient Estimate Variance
While introducing a baseline does not change the overall variance of the total rewards , it is crucial for reducing the variance of the gradient estimates. Subtracting the baseline from the total rewards reduces fluctuations around their mean, which makes the gradient estimates more stable. In general, this centers the rewards around zero, leading to reduced variance in the product .
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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 Estimate with Baseline
Baseline's Role in Centering Rewards and Reducing Gradient Variance
State-Value Function as a Baseline
Baseline's Impact on Reward Variance vs. Gradient Estimate Variance
An engineer is training two reinforcement learning agents (Agent A and Agent B) on the same task using a policy gradient method. The environment has a wide range of possible total rewards, from highly negative to highly positive. Agent A's learning algorithm directly uses the total reward received after each episode to update its policy. Agent B's algorithm first subtracts a constant value (equal to the average total reward observed so far) from the total reward before using it for the update. What is the most likely difference in the training process between Agent A and Agent B?
Benefit of a Baseline in a Positive-Reward Environment
A reinforcement learning agent is being trained in a specialized environment where the total reward for any complete episode consistently falls within a narrow range of 95 to 105. The training algorithm uses a policy gradient method and incorporates a baseline by subtracting the long-term average reward (approximately 100) from each episode's total reward before performing an update. Which statement best evaluates the utility of this baseline in this specific scenario?
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An engineer training an agent with a policy gradient method notices that the learning process is unstable due to high variance in the gradient estimates. To address this, they introduce a baseline which is subtracted from the rewards. What is the expected statistical consequence of this modification?
Differential Impact of Baselines on Variance
Introducing a baseline into a policy gradient algorithm is an effective technique for reducing the variance of the total rewards collected by the agent, which in turn stabilizes the learning process.