Data to Actions
We use the latest deep reinforcement learning algorithms to train agents to determine sensible actions from raw data of an environment. Initial testing occurs on historical data and the agents then evolve by adjusting to how they perform in the real-world.
We continuously perform rigorous automated testing of all our agents. We use sensitivity analysis with real and augmented data to work out the critical inputs that determine the actions our agents make.
Explanations by NLG
The final part of our pipeline is to convert our action analytics into natural language to provide reports for human readers. These explanations provide human operators with the confidence to follow the advise provided by the artificial agents.