Policy Rollout

A policy rollout is a single episode of executing a trained policy on the robot (or in simulation) from an initial state to task completion or timeout. Rollouts are used to evaluate policy performance, collect new data for further training (as in DAgger or RL fine-tuning), and debug failure modes. The number of rollouts needed for reliable performance estimation depends on task variability — high-variance tasks may require 50+ rollouts to get a stable success rate estimate. In research, rollouts are often categorized by initial condition (in-distribution vs. out-of-distribution objects/scenes) to characterize generalization.