From ai.facebook.com
![](https://scontent.flhr6-1.fna.fbcdn.net/v/t39.2365-6/74182736_524565368117455_6223615492616093696_n.jpg?_nc_cat=101&_nc_oc=AQn-kQkrbs_c_rrIHTWYXYnTerojie4sv-shttdxbgV0Ih_RJOCqPKJOUeqELrN0jKo&_nc_ht=scontent.flhr6-1.fna&oh=5c67783824633f4ff7650b1215c7c892&oe=5E237268)
Whether they’re designed to surface product recommendations or navigate busy highways, reasoning systems for real-world decision-making require some of the most sophisticated policies in machine learning. But despite advances in reinforcement learning (RL) and other reward-based approaches, learning through trial and error is difficult in unpredictable environments, and developing policies that can achieve complex objectives is often time- and resource-intensive. To overcome challenges like this, we are introducing ReAgent, a full suite of tools designed to streamline the process of building models that make and rely on decisions.