Baseline Method for Policy Gradient Variance Reduction
A straightforward technique to lower the variance of the policy gradient is to introduce a baseline, denoted as . This baseline acts as a reference point and is subtracted from the total reward, modifying the term to . By centering the rewards around this baseline (e.g., if is defined as the expected value of the total reward, this operation centers the rewards around zero), we remove systematic biases in the reward signal. This makes the learning updates more stable and less sensitive to extreme fluctuations in individual rewards without introducing bias.
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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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Baseline Method for Policy Gradient Variance Reduction
Total Reward (Return)
An agent is trained using a policy gradient method where the policy is updated based on the total reward of an entire trajectory. Consider two different trajectories that result in the same total reward:
- Trajectory A: The agent receives a small, consistent reward of +1 at each of 10 steps, for a total reward of +10.
- Trajectory B: The agent receives a reward of 0 for the first 9 steps and a large reward of +10 at the final step, for a total reward of +10.
Which of the following statements best analyzes the impact of these reward distributions on the policy update?
Diagnosing Unstable Reinforcement Learning Training
True or False: In a basic policy gradient method, if an agent completes a trajectory with a high positive total reward, the learning algorithm will reinforce every action taken during that trajectory, even those that were suboptimal or did not directly contribute to the final outcome.
Impact of Reward Scale Variation on Policy Gradient Variance
Baseline Method for Policy Gradient Variance Reduction
An agent is being trained in an environment where its sole objective is to maximize the sum of rewards it collects during an episode. The agent completes two separate episodes, receiving the following sequences of rewards:
- Episode A:
[+2, +2, +2, +2, +2] - Episode B:
[-5, -5, +10, +10, +1]
Based on the agent's primary objective, which statement correctly compares the outcomes of these two episodes?
- Episode A:
Robot Navigation Path Selection
Calculating Episode Return
Learn After
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?