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Related Concept Videos

Systematic Error: Methodological and Sampling Errors01:15

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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Propagation of Uncertainty from Systematic Error01:10

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
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Bellman error centering.

Xingguo Chen1, Yu Gong1, Jinguo Ye1

  • 1Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, Nanjing University of Posts & Telecommunications, Nanjing, 210023, China.

Neural Networks : the Official Journal of the International Neural Network Society
|April 2, 2026
PubMed
Summary
This summary is machine-generated.

This study re-examines reward centering methods in reinforcement learning (RL), introducing Bellman Error Centering (BEC) as a novel interpretation. New algorithms, Centered Temporal Difference (CTD) and CTDC, show improved stability and performance in RL tasks.

Keywords:
Bellman error centeringReinforcement learningTemporal difference learning

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Reinforcement Learning

Background:

  • Reward centering is crucial for stable reinforcement learning (RL).
  • Existing methods like Simple Reward Centering (SRC) and Value-based Reward Centering (VRC) have limitations.
  • A deeper theoretical understanding of VRC is needed.

Purpose of the Study:

  • To re-examine and clarify reward centering methods in RL.
  • To introduce Bellman Error Centering (BEC) as a novel reinterpretation of VRC.
  • To develop new algorithms based on BEC for improved RL performance.

Main Methods:

  • Mathematical analysis to prove VRC's equivalence to Bellman Error Centering (BEC).
  • Derivation of centered fixed-point solutions for tabular value functions and linear function approximation.
  • Development of on-policy Centered Temporal Difference (CTD) and off-policy CTDC algorithms.

Main Results:

  • VRC is mathematically equivalent to BEC, clarifying its mechanism.
  • Theoretical advances include centered fixed-point solutions.
  • Proposed CTD and CTDC algorithms demonstrate convergence under standard assumptions.
  • Experimental results show CTD and CTDC outperform baselines in stability and performance.

Conclusions:

  • The BEC paradigm offers a unified and effective approach to reward centering in RL.
  • CTD and CTDC algorithms provide stable and high-performing solutions for RL tasks.
  • BEC facilitates seamless integration with existing RL architectures, ensuring broad applicability.