From ai.facebook.com
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.