Robot Learning

Offline Reinforcement Learning (Offline RL)

Offline reinforcement learning (offline RL) is the training of policies from a fixed, previously collected dataset without further environment interaction. Its central challenge is distributional shift: naive value estimation overestimates out-of-distribution actions, which algorithms like CQL, IQL, and TD3+BC counteract with conservatism or behavior regularization. Benchmarks such as D4RL standardized evaluation in the field.

Why it matters for physical AI

Robots cannot cheaply explore in the real world, so learning to improve on logged demonstrations and past deployments without new trials is a key route to scalable policy improvement.