Robot Learning

Episode

Episode is a single complete interaction sequence between an agent and its environment, running from an initial state to termination by task success, failure, or a time limit, followed by a reset. In reinforcement learning, returns and success rates are computed per episode; in robot data collection, an episode typically corresponds to one demonstration of a task. Episode boundaries define the units over which policies are evaluated and datasets are organized.

Why it matters for physical AI

Resets are cheap in simulation but expensive on hardware, where a human often must restage the scene, making episode design and reset-free learning active concerns for scaling real-world data collection.