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Transformers in Distribution System01:27

Transformers in Distribution System

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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Transformers with Off-Nominal Turns Ratios01:25

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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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Reinforcement01:23

Reinforcement

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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
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Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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Hydraulic Jump: Problem Solving01:16

Hydraulic Jump: Problem Solving

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To analyze a hydraulic jump in a rectangular channel with a flow speed of 6 meters per second, follow these steps:Calculate Effective Upstream Velocity:When the downstream gate closes, a hydraulic jump forms, traveling upstream at 2 meters per second. This wave speed combines with the initial channel flow velocity, creating an effective upstream velocity.Identify Flow Velocities Before and After the Hydraulic Jump:Upstream of the hydraulic jump, the effective flow velocity includes both the...
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Related Experiment Video

Updated: Jul 16, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Transformer Decoder-Based Enhanced Exploration Method to Alleviate Initial Exploration Problems in Reinforcement

Dohyun Kyoung1, Yunsick Sung2

  • 1Department of Autonomous Things Intelligence, Graduate School, Dongguk University-Seoul, Seoul 04620, Republic of Korea.

Sensors (Basel, Switzerland)
|September 9, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel reinforcement learning method using a pretrained transformer decoder to significantly reduce initial exploration. This approach accelerates learning and improves performance, achieving higher rewards and win rates compared to traditional strategies.

Keywords:
explorationmachine learningpretrainingreinforcement learningtransformer-decoder

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Reinforcement Learning

Background:

  • Epsilon-greedy strategy is a common exploration technique in reinforcement learning.
  • This strategy often results in extensive initial exploration and prolonged learning periods.
  • Current methods to reduce exploration, like using expert data, have limitations in reducing the initial exploration range.

Purpose of the Study:

  • To propose a novel method for reducing the initial exploration range in reinforcement learning.
  • To enhance learning efficiency and agent performance by guiding early actions.
  • To improve upon existing exploration techniques in reinforcement learning.

Main Methods:

  • Pretraining a transformer decoder with extensive expert data.
  • Using the pretrained model to guide agent actions during initial learning stages.
  • Transitioning to the epsilon-greedy strategy after a learning threshold is met.

Main Results:

  • The proposed method demonstrated approximately 2.5 times the average reward in the FreeStyle1 basketball game.
  • Achieved a 26% higher win rate compared to the traditional Deep Q-Network (DQN) with epsilon-greedy strategy.
  • Effectively reduced the initial exploration range and optimized learning times.

Conclusions:

  • The pretrained transformer decoder method significantly enhances reinforcement learning performance.
  • This approach offers a substantial improvement over traditional exploration techniques.
  • The method effectively balances exploration and exploitation for faster, more efficient learning.