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Related Concept Videos

Reinforcement Schedules01:24

Reinforcement Schedules

231
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
231
Timing and Consequences on Behavior01:08

Timing and Consequences on Behavior

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In operant conditioning, the timing of reinforcement is crucial. For animals like rats and cats, immediate reinforcement (within a few seconds) is much more effective than delayed reinforcement. For example, a food reward for a rat needs to follow within 30 seconds of pressing a bar to be effective. 
Humans, however, can respond to delayed reinforcers. We often make decisions between immediate small rewards and delayed larger rewards. This ability to delay gratification is a significant...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Expected Value01:15

Expected Value

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The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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Regression Toward the Mean01:52

Regression Toward the Mean

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Related Experiment Video

Updated: Aug 28, 2025

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
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Minimax Optimal Bandits for Heavy Tail Rewards.

Kyungjae Lee, Sungbin Lim

    IEEE Transactions on Neural Networks and Learning Systems
    |September 14, 2022
    PubMed
    Summary

    This study introduces new algorithms, MR-UCB and MR-APE, for heavy-tailed stochastic multiarmed bandits (MABs). These methods achieve optimal regret bounds, outperforming existing approaches for decision-making with noisy, heavy-tailed rewards.

    Area of Science:

    • Decision Sciences
    • Machine Learning
    • Reinforcement Learning

    Background:

    • Stochastic multiarmed bandits (MABs) typically assume bounded or light-tailed reward distributions.
    • Heavy-tailed reward distributions are common in real-world decision-making but pose challenges for existing MAB algorithms.
    • Prior exploration methods fail to guarantee minimax optimal regret bounds for heavy-tailed rewards.

    Purpose of the Study:

    • To address the limitations of existing methods in stochastic MABs with heavy-tailed rewards.
    • To develop novel algorithms that achieve minimax optimal regret bounds under heavy-tailed reward distributions.
    • To theoretically and empirically demonstrate the superiority of the proposed methods.

    Main Methods:

    • Theoretical analysis of sub-optimality for existing exploration methods in heavy-tailed MABs.

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  • Proposal of Minimax Optimal Robust Upper Confidence Bound (MR-UCB) using a tight confidence bound for a p-robust estimator.
  • Development of Minimax Optimal Robust Adaptively Perturbed Exploration (MR-APE), a randomized variant of MR-UCB, independent of the p-th moment bound (νp).

    • Main Results:

    • Existing exploration methods are shown to be suboptimal for heavy-tailed rewards.
    • MR-UCB and MR-APE achieve minimax optimal regret bounds for heavy-tailed stochastic MABs.
    • The proposed methods demonstrate superior performance in simulations using Pareto and Fréchet noises compared to existing algorithms.

    Conclusions:

    • MR-UCB and MR-APE are the first algorithms to theoretically guarantee minimax optimality for heavy-tailed stochastic MAB problems.
    • These novel methods offer significant improvements for sequential decision-making scenarios with heavy-tailed reward distributions.
    • The findings advance the field of reinforcement learning by providing robust solutions for challenging reward structures.