Research
Cooperation among learning agents
My doctoral research studies how cooperation emerges — or fails to emerge — among self-interested
agents trained with reinforcement learning. In semi-cooperative settings, agents share an environment
but not a common objective; my work designs incentive structures, such as reputation mechanisms and
filtered reward shaping, that align individual goals with collective outcomes in high-dimensional
environments.
Doctoral research
Reputation mechanisms for semi-cooperative multi-agent RL
2024 — present
AI Movement, Mohammed VI Polytechnic University (UM6P)
Starting from a semi-cooperative prey–predator game, this line of work introduces a reputation
mechanism and filtered reward-shaping functions that encourage agents with heterogeneous abilities
to cooperate. Our approach is evaluated against established baselines such as IQL, Partial Q-MIX,
and PED-DQN, and was published at AAMAS 2025 with an application to collaborative target
acquisition and coverage optimization in satellite constellations.
MARLReward shapingReputationSatellite constellations
Selected projects
Embodying a qubit's mind with stochastic deep RL and quantum computing
2022 — 2023
ENSAM Meknès
A mathematical framework for studying qubits — Hilbert spaces, density matrices, Bell states, and
reversibility under entanglement — combined with a Q-learning agent. Real measurement data were
collected from IBM Quantum Lab, with circuits built in Qiskit, to predict a qubit's next spin with a
classification model.
Quantum computingQiskitQ-learning
Teaching quantum agents to stay entangled inside a wormhole
2023
Independent project
An exploration of the ER = EPR conjecture through the lens of reinforcement learning: studying the
entanglement of qubits on opposite sides of an Einstein–Rosen bridge, information flow at the event
horizon, and a QOMDP-based RL model simulating the experiment on a holographic wormhole.
Quantum RLQOMDPEntanglement
Time-complexity optimization for Branch & Bound via machine learning
2023
ENSAM Meknès
On the Travelling Salesman Problem, K-means clustering of scattered instances is used to reduce the
effective time complexity of the Branch & Bound algorithm, with a systematic comparison of the
clustered and exact approaches.
Combinatorial optimizationTSPClustering
Optimizing Q-learning on Atari games
2023
ENSAM Meknès
Improvements to Q-learning agents on Pong and Breakout through a target network and learning-rate
scheduling, benchmarked against DQN and Double DQN.
Deep RLDQNAtari
Sun-tracking model for an intelligent energy cell
2023 — 2024
ENSAM Meknès × UQAR (Canada)
A custom Gym environment models a solar energy cell; reinforcement learning policies are trained to
maximize captured solar energy under different conditions.
RLGymRenewable energy
AI-driven workforce schedule optimizers
2023 — 2024
ENSAM Meknès
A customized scheduling problem solved with genetic algorithms, the Hungarian algorithm, linear
programming, and SVMs on generated data — delivered as a Streamlit app displaying smart schedules
alongside statistical analyses of productivity and fatigue.
SchedulingGenetic algorithmsStreamlit
Smart electronic voting with blockchain and big data
2023 — 2024
ENSAM Meknès
Election data stored off-chain with IPFS, a Solidity smart contract for vote counting tested on
Tenderly and validated on Ethereum, and analysis of validated votes with Apache Spark and
MapReduce.
BlockchainSolidityApache Spark
Twin and cousin primes in the forming of all even numbers
2022
Independent project
Building on Goldbach's and Polignac's conjectures, this project proposes two new conjectures
relating twin and cousin primes to the decomposition of even numbers, with algorithms for checking
their validity and a study of the correlations between them.
Number theoryConjectures
Industry experience
Deep RL for satellite constellations — research intern
Feb — Jul 2024
AI Movement (UM6P), Rabat
Collaborative target acquisition and coverage optimization applied to a satellite constellation;
the internship that grew into my current doctoral research and our AAMAS 2025 paper.
Quality control for DAP fertilizer manufacturing — intern
Aug — Sep 2023
OCP Group (DAP/JFC4), El Jadida
Six Sigma methodology applied to enhance a control system, real-time Power BI dashboards fed by
push datasets from SQL Server, and an interactive Flask interface with embedded reports providing
corrective recommendations to operators in case of anomalies.
Predictive maintenance with recurrent neural networks — intern
Jul — Aug 2022
Nestlé Factory, El Jadida
An LSTM pipeline for sensor data — collection, smoothing, correlation analysis, and dimensionality
reduction — with model evaluation and a study of time and space complexity.