Benefit of a Baseline in a Positive-Reward Environment
A reinforcement learning agent is being trained in an environment where the total reward for any complete episode is always a large positive number, ranging from +500 to +1000. An engineer decides to modify the learning algorithm by subtracting a baseline value of 750 (the average reward) from the total reward before updating the policy. Explain why this modification is likely to improve the stability of the training process, even though all rewards are already positive.
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
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Analysis in Bloom's Taxonomy
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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?