demonstrationstasks

Demonstration tasks are tasks that are designed to elicit human or agent demonstrations of desired behavior, with the goal of enabling learning from those demonstrations. In fields such as machine learning, robotics, and human–computer interaction, demonstration tasks are used as a source of training data and as benchmarks to evaluate how well learning systems can imitate or generalize from expert behavior. They typically involve sequences of states and actions that illustrate a preferred solution or control policy.

A demonstration task is usually described by starting conditions, a goal or objective, and a sequence of decisions that lead from the start to the goal. The collected demonstrations are stored as trajectories, consisting of state-action pairs (and sometimes rewards or timing information). These data can be real demonstrations provided by experts, crowdsourced inputs, or generated in simulated environments. They may be discrete or continuous in action space and can vary in complexity, duration, and diversity.

Applications of demonstration tasks include imitation learning, apprenticeship learning, and inverse reinforcement learning, where algorithms infer policies, cost functions, or reward structures from the demonstrated behavior. They also serve as standardized testbeds for comparing learning algorithms, and as curriculum tasks that gradually increase in difficulty to support skill acquisition in robots or software agents.

Challenges in using demonstration tasks include ensuring demonstration quality, addressing distribution and covariate shift between demonstrations and deployment, and handling ambiguity or suboptimal demonstrations. Effective use often involves augmenting demonstrations with exploration, using probabilistic models of uncertainty, and combining demonstrations with additional learning signals.

See also: imitation learning, apprenticeship learning, programming by demonstration.