This repository provides a full implementation of the DEEP DOCTR-L algorithm, based on:

“Distributional Offline Continuous-Time Reinforcement Learning with Neural Physics-Informed PDEs” – Igor Halperin (2023)

It was developed as part of the course Machine Learning and Stochastic Control in the Master Probabilités et Finance at Sorbonne Université.

The repository includes:

• The complete code to reproduce the original experiments described in the paper
• A main script to run the full pipeline: data generation, model training, and evaluation

• Original Paper
• Project Report — presents the method, key derivations, and additional experiments comparing DOCTR-L to a semi-closed-form Riccati solution

🔧 The Riccati-based experiments from the report are not included in this repository.