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Gradient estimator

As explained in Optimization Algorithms: AI techniques for design, planning, and control problems

A gradient estimator is a computed approximation of a gradient based on finite samples or other available information.

A gradient estimator approximates the policy gradient from a finite batch of reinforcement-learning samples. Policy-gradient methods use this estimate with stochastic gradient ascent to favor actions producing higher returns. section 12.1.2

The estimator connects observed outcomes to policy updates:

  • Computes an empirical average over a finite sample batch rather than requiring an exact expectation. section 12.1.2
  • Weights the policy contribution with an advantage estimate, comparing an action’s expected return with the state’s estimated value. section 12.1.2
  • Feeds the estimate into stochastic gradient ascent, increasing the likelihood of actions with positive advantage and decreasing it for negative advantage. section 12.1.2

Positive and negative advantage

When an observed return exceeds the state’s expected value, the advantage is positive and the gradient raises that action’s future probability. A negative advantage lowers it. section 12.1.2

How Optimization Algorithms: AI techniques for design, planning, and control problems covers it

How deep it goes
Explains empirical averaging, policy loss, advantage estimates, and stochastic gradient ascent, while emphasizing intuition over formal derivation.
Examples
Uses positive and negative advantage cases to show how observed returns change future action probabilities.
What you can do after
Apply it via stochastic gradient ascent to increase high-advantage action probabilities and reduce low-advantage action probabilities.
How it connects
Connects policy gradients with reinforcement learning, stochastic gradient ascent, advantage functions, Q-values, and value functions.
Cover of Optimization Algorithms: AI techniques for design, planning, and control problems
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