RL Framework
Reinforcement Learning Environments
Build custom reinforcement learning environments that train AI agents to optimize complex business decisions like pricing, scheduling, and logistics.
- you own the policy
- open-weight
- LoRA adapters
- simulated first
- Control
- You keep the reins
- You define the objective, the reward, and the constraints. We handle the environment, training, and infrastructure.
- LoRA
- Open-weight policies
- Agents fine-tuned on open-weight models with LoRA, so iteration is fast and the weights are yours.
- Sim-first
- Test before deploy
- Thousands of scenarios run in simulation before anything touches production.
02 / The framework
You bring the decision. We bring the training.
A training framework for builders. You stay in control of what the agent optimizes for; we handle the environment, the loop, and the infrastructure.
You control
- The decision and its constraints
- The reward, and what “good” means
- Your data and how it is evaluated
- When a policy is ready to ship
We handle
- The simulation environment
- The training loop and reward modeling
- Compute and infrastructure
- A trained policy you own and can self-host
03 / The loop
How an agent learns the policy
Define the task and the reward, run episodes in simulation, and ship a policy you own, with a human in the loop on the edge cases.
- TriggerTask definitionThe behavior you want an agent to learn.
- AI stepSimulate + rewardAn environment runs episodes and scores them.
- IntegrationTraining loopIt connects to your learner and infra.
- OutputTrained policyAn agent that does the task, with logs to prove it.
04 / What it changes
What the build is designed to do
- 01Discover optimal strategies for complex decisions that defy simple rules
- 02Continuously adapt strategies as market conditions change
- 03Test thousands of scenarios in simulation before deploying in the real world
- 04Handle multi-variable optimization that would overwhelm human decision-makers
- 05Achieve measurably better outcomes than static rules or manual management
05 / Recipes
Where teams point it
A few of the decisions teams hand to a trained agent first.
- 01A local hotel uses RL to dynamically adjust room pricing based on occupancy, local events, competitor rates, and booking patterns, increasing revenue per available room by 12%
- 02A restaurant group trains an RL agent to optimize staff scheduling across locations, balancing labor costs against service quality metrics and predicted customer volume
- 03A delivery service uses RL to optimize routing and order batching in real-time, reducing delivery times by 20% while cutting fuel costs
- 04A local retailer uses RL to optimize markdown timing and depth for seasonal inventory, maximizing recovery while clearing stock before it becomes obsolete
08 / FAQs
Reinforcement Learning Environments questions
What is reinforcement learning and how is it different from other AI?
Reinforcement learning is a type of AI where an agent learns by taking actions in an environment and receiving feedback in the form of rewards or penalties. Unlike supervised learning, which requires labeled examples of correct answers, RL discovers optimal strategies through exploration and experimentation. Think of it like training a new employee by letting them try different approaches and giving them feedback, rather than giving them a manual of exact instructions. RL excels at sequential decision-making problems where the best action depends on the current situation.
How do you build a simulation environment for my business?
We start by deeply understanding your business operations, decision points, and objectives. We then build a digital simulation that models your key dynamics, customer arrival patterns, demand fluctuations, resource constraints, competitor behavior, and cost structures. The simulation is calibrated using your historical data so it accurately reflects your real operating environment. We validate the simulation by comparing its outputs to actual historical outcomes before using it to train RL agents. The simulation becomes a valuable asset you can use for ongoing strategy testing.
How long does it take to see results from RL optimization?
Building the simulation environment and training the initial RL agent typically takes 6-10 weeks. The agent can then be deployed in a limited pilot, for example, managing pricing for a subset of products or scheduling for one location, within days of training completion. Most clients run a 2-4 week pilot to validate the agent's decisions against human decisions or previous methods before expanding. Measurable improvements in the target metric, whether revenue, cost, or efficiency, are typically visible within the first month of deployment.
Is reinforcement learning risky? What if the agent makes bad decisions?
We implement multiple safety guardrails to prevent bad decisions. Every RL agent operates within defined bounds, for example, a pricing agent cannot set prices below cost or above a specified ceiling. During initial deployment, the agent's decisions are reviewed by a human before execution. We also run extensive testing in simulation before any real-world deployment, and monitor agent performance continuously with automated alerts if outcomes deviate from expectations. The agent can be paused instantly and reverted to manual control at any time.
Turn Reinforcement Learning Environments into something your team actually uses.
Name the work you want this to handle. We will map the build, show what is worth doing first, and what it costs. If there is no fit, we will say so.