3 research outputs found
Multi-Task Imitation Learning for Linear Dynamical Systems
We study representation learning for efficient imitation learning over linear
systems. In particular, we consider a setting where learning is split into two
phases: (a) a pre-training step where a shared -dimensional representation
is learned from source policies, and (b) a target policy fine-tuning step
where the learned representation is used to parameterize the policy class. We
find that the imitation gap over trajectories generated by the learned target
policy is bounded by , where is the state
dimension, is the input dimension, denotes the
total amount of data collected for each policy during representation learning,
and is the amount of target task data. This result
formalizes the intuition that aggregating data across related tasks to learn a
representation can significantly improve the sample efficiency of learning a
target task. The trends suggested by this bound are corroborated in simulation.Comment: Appeared in L4DC 2023. V3: corrected typo in assumption