Offline RL is reinforcement learning from a fixed dataset of pre-collected trajectories, without generating new interactions — cheaper but the policy can only learn from behaviors already in the data. This approach is useful when generating new data is expensive or risky, but requires careful handling to avoid learning poor behaviors from low-quality trajectories.