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Imitation Learning

Imitation Learning is a framework for learning a behavior policy from demonstrations. Usually, demonstrations are presented in the form of state-action trajectories, with each pair indicating the action to take at the state being visited. In order to learn the behavior policy, the demonstrated actions are usually utilized in two ways. The first, known as Behavior Cloning (BC), treats the action as the target label for each state, and then learns a generalized mapping from states to actions in a supervised manner. Another way, known as Inverse Reinforcement Learning (IRL), views the demonstrated actions as a sequence of decisions, and aims at finding a reward/cost function under which the demonstrated decisions are optimal.

Finally, a newer methodology, Inverse Q-Learning aims at directly learning Q-functions from expert data, implicitly representing rewards, under which the optimal policy can be given as a Boltzmann distribution similar to soft Q-learning

Source: Learning to Imitate

Papers

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Showing 1641–1650 of 2122 papers

TitleStatusHype
On Computation and Generalization of Generative Adversarial Imitation Learning—0
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning—0
On Efficient Online Imitation Learning via Classification—0
One-Shot Dual-Arm Imitation Learning—0
One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks—0
One-Shot Imitation Filming of Human Motion Videos—0
One-shot Imitation in a Non-Stationary Environment via Multi-Modal Skill—0
One-Shot Imitation Learning—0
One-Shot Imitation Learning: A Pose Estimation Perspective—0
One-shot Imitation Learning via Interaction Warping—0
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