Policy Gradient Estimate with Baseline
The policy gradient model can be refined by incorporating a baseline, , to reduce variance. The baseline is subtracted from the total reward for each trajectory. Estimated from a dataset of trajectories, the formula for the policy gradient with a baseline is given by: This adjustment helps stabilize the learning process by reducing the variance of the gradient estimate, making updates less sensitive to extreme fluctuations in individual rewards.
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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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Derivation of Reward Decomposition in Policy Gradient with Baseline
Unbiased Nature of Policy Gradient with Baseline
In a reinforcement learning task, an agent completes two distinct trajectories. Trajectory A results in a total reward of +20, and Trajectory B results in a total reward of +5. To update the agent's policy, a baseline value of +12 is subtracted from each trajectory's total reward. Based on this information, how will the policy updates derived from these two trajectories differ?
Consider the formula for the policy gradient estimate with a baseline: According to this formula, the baseline value
bis subtracted from the rewardr_tat each individual timesteptwithin a trajectory to reduce variance.Stabilizing Policy Gradient Training