Temporal-difference error
A temporal-difference error is the difference between a target formed from an immediate reward and discounted successor-value estimate and the current value estimate.
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 it is used
- Guides Q-learning updates for financial-planning decisions in a model-free reinforcement-learning setting.
- 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 you can do after
- Apply it via Python to update Q values while learning financial-planning policies from sampled transitions.
Read how Build a Robo-Advisor with Python (From Scratch) explains temporal-difference error →
Reinforcement Learning from Human Feedback
A temporal-difference residual is the difference between an observed immediate reward plus a bootstrapped next-state value and the current value prediction.
- How it is used
- Uses the residual to estimate advantages and drive value-function updates within policy-gradient reinforcement learning.
- How deep it goes
- Defines the residual mathematically and derives its role in GAE, including backward accumulation, terminal masking, and value targets.
- Examples
- Uses the TD residual in token-level GAE code with rewards, value predictions, gamma, lambda, and terminal done masks.
- What you can do after
- Apply it via token-level GAE to compute policy advantages and value-regression targets from rewards and value predictions.
Read how Reinforcement Learning from Human Feedback explains temporal-difference error →
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.
- errorkind of · Prediction error is the discrepancy between a model’s predicted outcome and the corresponding actual or observed outcome.
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