Robot Learning

Stochastic Policy

A stochastic policy is a policy that defines a probability distribution over actions for each state and samples from it, rather than returning a single deterministic action. Gaussian policies over continuous torques or end-effector deltas are standard in policy gradient and maximum-entropy RL, where stochasticity drives exploration and smooths optimization; diffusion policies represent richer multimodal action distributions. Deployed systems often act on the distribution's mean or a low-temperature sample.

Why it matters for physical AI

Human demonstrations are multimodal — there are many valid ways to grasp a cup — and policies that model action distributions rather than averages avoid committing to invalid compromises.