Stochastic gradient ascent is an iterative optimization method that increases an objective using gradient estimates computed from sampled data.
Stochastic gradient ascent iteratively increases an objective using a gradient estimate computed from sampled experience. In reinforcement learning, policy-gradient methods use it to adjust policy parameters toward higher expected cumulative reward. section 12.1.2
The policy-gradient update follows this intuition:
Estimate the policy gradient from a finite batch of samples using the policy-gradient loss. section 12.1.2
Increase the likelihood of actions with positive advantage, meaning their observed returns exceed the state’s expected value. section 12.1.2
Reduce preference for actions with negative advantage, progressively refining the stochastic policy through interaction with the environment. section 12.1.2
How Optimization Algorithms: AI techniques for design, planning, and control problems covers it
How deep it goes
Explains the update intuition through finite-batch gradient estimates, advantage values, and action-probability changes, without implementation details.
What you can do after
Apply it via policy-gradient training to increase the probability of actions associated with higher returns.
How it connects
Connects policy gradients with reinforcement learning, stochastic policies, advantage functions, Q-values, and state-value functions.
stochastic gradient descentcontrasts with · Stochastic gradient descent is an iterative optimization method that updates parameters using gradients estimated from individual examples or randomly selected small batches.
This explainer was generated by Manning's knowledge graph from the text of Optimization Algorithms: AI techniques for design, planning, and control problems. The section links point to the parts of the book it draws on.
