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Updated: Nov 11, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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Diversity-driven knowledge distillation for financial trading using Deep Reinforcement Learning.

Avraam Tsantekidis1, Nikolaos Passalis1, Anastasios Tefas1

  • 1School of Informatics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece.

Neural Networks : the Official Journal of the International Neural Network Society
|March 28, 2021
PubMed
Summary
This summary is machine-generated.

This study enhances financial trading agents by using neural network distillation with diverse teacher models. This novel approach improves training stability and performance in noisy markets.

Keywords:
Deep Reinforcement LearningFinancial marketsTrading

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A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Finance

Background:

  • Deep Reinforcement Learning (DRL) is widely applied to financial trading but faces challenges with optimization instability in noisy environments.
  • Existing DRL methods struggle to achieve reliable performance due to the inherent volatility of financial markets.

Purpose of the Study:

  • To introduce a novel method for improving the training reliability and performance of DRL trading agents.
  • To leverage neural network distillation with diversified teacher agents to enhance stability and exploration in financial trading.

Main Methods:

  • Developed a DRL training framework utilizing neural network distillation.
  • Trained diverse "teacher" agents on different RL environment subsets to learn varied policies.
  • Trained "student" agents via distillation from these teachers to guide exploration and mimic profitable strategies.
  • Employed diversified ensembles of teachers specializing in different currencies for enhanced policy transfer.

Main Results:

  • Diversified teacher ensembles significantly improve the performance of student DRL trading agents.
  • The proposed distillation method enhances training stability in noisy financial environments.
  • Experimental evaluations on financial trading tasks demonstrate substantial performance gains.
  • Generality of the method is validated through experiments in game control using Procgen environments.

Conclusions:

  • Neural network distillation with diversified teachers offers a robust solution for training reliable DRL trading agents.
  • The approach effectively mitigates instability issues in financial DRL applications.
  • This method shows promise for advancing automated trading strategies and has broader applicability in RL domains.