Mastering RL-Ops with AlphaSwarm

Reinforcement Learning (RL) has shown incredible promise in finance, but moving from a successful notebook experiment to a stable production deployment is notoriously difficult. This is the challenge of RL-Ops.

The Challenges of RL in Production

Unlike traditional supervised learning, RL agents interact with their environment, making them sensitive to distribution shift and execution nuances.

1. Trajectory Storage

AlphaSwarm uses Apache Iceberg to store every step of an agent's trajectory. This allows for massive-scale offline reinforcement learning and robust evaluation.

2. PRUDEX Evaluation

We implement the PRUDEX framework to evaluate agents across multiple dimensions: Performance, Risk, Understanding, Diversity, Experience, and Generalization.