Robot Learning

Policy Gradient

Policy gradient methods are reinforcement learning algorithms that directly optimize expected return by ascending its gradient with respect to policy parameters, following the REINFORCE estimator of Williams (1992) and its actor-critic refinements. Because they optimize stochastic policies over continuous action spaces without discretization, they suit robot control, with trust-region variants such as TRPO and PPO taming the high variance of the gradient estimates.

Why it matters for physical AI

Continuous, high-dimensional action spaces make policy gradients the default family for training locomotion and manipulation controllers in simulation, where massive parallelism offsets their sample inefficiency.