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Integrating Contractive Loss in Proximal Policy Optimization for Robust Decision-Making

This repository presents an open-source model designed to integrate contractive loss in the training of financial trading models within a Deep Reinforcement Learning (DRL) pipeline using the PPO algorithm. The core contribution is the implementation of the integration of the contractive loss in the PPO algorithm for the training of more profitable and robust financial trading agents.

Example

A ready to use training example is provided: python3 train_rl.py

Acknowledgments

This work has received funding from the research project ”Energy Efficient and Trustworthy Deep Learning - DeepLET” is implemented in the framework of H.F.R.I call “Basic research Financing (Horizontal support of all Sciences)” under the National Recovery and Resilience Plan “Greece 2.0” funded by the European Union – NextGenerationEU (H.F.R.I. Project Number: 16762). This publication reflects the authors’ views only. The European Commission is not responsible for any use that may be made of the information it contains.

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