Reinforcement Learning
Systems that learn from consequences.
When the problem has no label but a goal — cut delays, maximize throughput, catch anomalous behaviour that evolves — a reinforcement learning agent learns the best policy by interacting with data and simulations, and keeps learning in production.
In practice
Problem formulation
States, actions, reward: we translate the business goal into a sequential decision problem, with explicit constraints on what the agent must never do.
Simulation first, reality next
We train in simulated environments built on historical data, then move to the real world with continuous learning and safety limits.
Continuous training
Pipelines that update the policy with new data, evaluate drift and always keep a stable version to fall back to.
Operational explainability
Dashboards that show why the agent flags or decides: essential for the people who must trust the system every day.
Stack and technologies
- PyTorch
- Gymnasium
- Python
- NVIDIA GPU
- NumPy
Project
In GA.IAShipment anomalies, inside GA.IA
Inside GA.IA, our supply chain engine, a reinforcement learning agent detects shipment anomalies and trains continuously on real data: it learns normal delivery patterns, flags deviations and adapts when the context changes.
Discover GA.IA →- Where
- GA.IA · shipments module
- What
- anomaly detection
- How
- continuous training on real data
- Stack
- PyTorch
How we work
Four steps, always the same
- 01
Definition of states, actions and reward with the operations team
- 02
Simulation environment from historical data, offline training
- 03
Controlled release with safety limits and monitoring
- 04
Continuous learning and periodic policy review
Frequently asked questions
When does reinforcement learning make sense?
When decisions are sequential and their effect unfolds over time — scheduling, allocation, pricing, control — or when anomalies evolve and a static model ages quickly. For problems with clear labels, supervised models remain preferable.
Is it risky in production?
We manage it with simulation, explicit action limits, gradual rollout and a stable policy always ready for rollback.
Related capabilities
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