Robot Learning

Model-Based Reinforcement Learning

Model-based reinforcement learning is a family of RL methods that learn an explicit model of environment dynamics and use it for policy improvement, either by generating imagined training rollouts, by planning through the model at decision time, or both. Dreamer learns latent world models and trains policies in imagination, while TD-MPC combines learned latent dynamics with short-horizon planning; both are markedly more sample-efficient than model-free baselines.

Why it matters for physical AI

Real robots cannot afford millions of trial-and-error interactions; learning dynamics from limited experience and practicing in imagination is the leading strategy for sample-efficient improvement on physical hardware.