Temporal-difference error

As explained in Build a Robo-Advisor with Python (From Scratch)

A temporal difference is the discrepancy between a current value estimate and a reward-adjusted estimate based on a subsequent state.

How Build a Robo-Advisor with Python (From Scratch) covers it

How deep it goes

Defines the signed discrepancy and implements its Q update, while deferring detailed transition-probability calculations.

Examples

Uses a wealth–time grid, random wealth transitions, Q-table updates, and repeated generated episodes.

What it assumes

Assumes Q-learning states and actions, while defining the discount rate, temporal difference, and learning rate alongside the update.

What you can do after

Apply it via Python to update Q values while learning financial-planning policies from sampled transitions.

How it connects

Connects Q-learning with discount rate, learning rate, exploration and exploitation, and model-free reinforcement learning.

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Also explained in

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  liveBookmanning.com
  Reinforcement Learning from Human FeedbackA temporal-difference residual is the difference between an observed immediate reward plus a bootstrapped next-state value and the current value prediction.

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Related concepts

• value functionuses · A value function assigns or estimates the expected cumulative reward, utility, or quality associated with a state or action under a policy or decision rule.
• generalized advantage estimationused by · Generalized advantage estimation estimates policy advantages by exponentially weighting temporal-difference errors across multiple time scales, trading bias against variance.

This explainer was generated by Manning's knowledge graph from the text of Build a Robo-Advisor with Python (From Scratch). The section links point to the parts of the book it draws on.