You will get a Reinforcement Learning agent in Python/PyTorch for your environment
Project details
You will get a working reinforcement learning agent implemented in Python/PyTorch,
tailored to your environment and use case. I am a final-year MSc Physics Engineering
student at the University of Coimbra, currently building a 4-agent MARL system for
autonomous control of a wastewater treatment plant — real-world RL, not toy examples.
I deliver clean, documented code with training curves and evaluation metrics.
Fully remote, async-friendly, and focused on results.
tailored to your environment and use case. I am a final-year MSc Physics Engineering
student at the University of Coimbra, currently building a 4-agent MARL system for
autonomous control of a wastewater treatment plant — real-world RL, not toy examples.
I deliver clean, documented code with training curves and evaluation metrics.
Fully remote, async-friendly, and focused on results.
Machine Learning Tools
MATLAB, NumPy, pandas, Python, PyTorchWhat's included
| Service Tiers |
Starter
$50
|
Standard
$150
|
Advanced
$350
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
Number of Graphs/Charts | 1 | 3 | 5 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | - | - | - |
Source Code | - |
Frequently asked questions
About Daniel
AI & Machine Learning | Simulink, Reinforcement Learning, PyTorch
Albufeira, Portugal - 3:47 am local time
My thesis involves building a G2ANet MARL system to autonomously control a wastewater treatment plant simulated in BSM2/Simulink — 4 cooperative RL agents trained with SAC in Python/PyTorch, communicating with MATLAB in real time.
What I can help you with:
- Reinforcement Learning implementation (SAC, PPO, multi-agent)
- Python/PyTorch modelling and simulation
- MATLAB/Simulink dynamic systems and control
- Robot kinematics, Jacobian control, trajectory planning
- Mathematical modelling and optimisation
I've also controlled a real 6-DOF robot arm (xArm Lite6) in hardware using Jacobian-based velocity control at 50 Hz — all code is public on GitHub.
Fluent in English, Portuguese, and Russian. Available for remote work with fully flexible scheduling.
Let's build something together.
Steps for completing your project
After purchasing the project, send requirements so Daniel can start the project.
Delivery time starts when Daniel receives requirements from you.
Daniel works on your project following the steps below.
Revisions may occur after the delivery date.
Review requirements and define architecture
Analyse your environment description, define the agent type (SAC, PPO, DQN or MARL) and confirm the approach with you before coding starts.
Implement and train the agent
Build the RL agent in PyTorch, set up the training loop, and run initial training on your environment.