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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.
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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.
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Distributed Loads: Problem Solving01:21

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Related Experiment Video

Updated: Sep 30, 2025

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
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Multi-Agent Reinforcement Learning Based Fully Decentralized Dynamic Time Division Configuration for 5G and B5G

Xiangyu Chen1, Gang Chuai1, Weidong Gao1

  • 1Department of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel, Switzerland)
|March 10, 2022
PubMed
Summary

Future network services require dynamic traffic adaptation. This study introduces a multi-agent reinforcement learning method for dynamic time division duplex configuration in 5G networks, optimizing uplink and downlink traffic efficiently.

Keywords:
5G and B5GMARLdecentralized networkdynamic TDDleniency control

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Area of Science:

  • Telecommunications Engineering
  • Artificial Intelligence
  • Network Optimization

Background:

  • Future network services demand adaptive uplink and downlink traffic management.
  • 5G New Radio (NR) requires dynamic adjustment of time-domain duplex patterns.
  • Configuring effective dynamic time division duplex (D-TDD) patterns for 5G NR remains an open research challenge.

Purpose of the Study:

  • To propose a decentralized D-TDD configuration method using distributed multi-agent deep reinforcement learning (MARL).
  • To maximize the sum rates of all users (UE) by optimizing the D-TDD configuration policy.
  • To reduce signaling overhead through a fully decentralized MARL approach.

Main Methods:

  • Modeling the D-TDD configuration as a dynamic programming problem and defining the policy as a conditional probability distribution.
  • Implementing a decentralized MARL solution where each base station (BS) acts as an agent, using local buffer length observations.
  • Integrating a leniency controller and a binary LSTM (BLSTM) based auto-encoder to address MARL's global information limitations and handle complex data.

Main Results:

  • The proposed distributed MARL method achieves stable convergence across diverse network environments.
  • The system effectively configures uplink and downlink time slot ratios based on local user queue buffer lengths.
  • The MARL approach, deployed on Mobile Edge Computing (MEC) servers, demonstrates superior performance compared to traditional distributed deep reinforcement algorithms.

Conclusions:

  • The developed distributed MARL framework provides an effective and stable solution for dynamic TDD configuration in 5G networks.
  • Decentralized decision-making based on local observations, enhanced by leniency control and auto-encoders, optimizes network resource allocation.
  • This approach offers a promising direction for enhancing the adaptability and efficiency of future wireless communication systems.