What is Reinforcement Learning
Reinforcement Learning is a machine learning technique to create mappings between situations and actions in order to achieve a set objective. The system is presented with states, takes actions, receives rewards and calculates how valuable such action was. If the reward is positive, the action gains value, if the reward is negative, the action loses value.
Think of Reinforcement Learning as a general framework to mathematically represent decision-making process and to automate solution discovery with machine.
With reinforcement learning you don’t need the labeled data. The agent will interact with the environment directly to search for the optimal solution, however not all use cases are appropriate and feasibility study is required.
If the use case is appropriate, we will design, validate and deploy the algorithm for your requirements.
Reinforcement Learning agents, like any other Machine Learning software, requires periodic maintanance to make sure it functions according to expectations.
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Sample Use Cases
We have gathered a list of already verified use-cases for Reinforcement Learning to give you some ideas.
- Grid Balancing
- Transient Stability
- Economic Dispatch
- HVAC Control
- Inventory Management Optimization
- eCommerce Personalization
- Marketing Optimization
- Delivery Optimization
- Scheduling Optimization
- Algorithmic Trading