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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 1731–1740 of 2122 papers

TitleStatusHype
Learning Compound Tasks without Task-specific Knowledge via Imitation and Self-supervised Learning—0
Reward-Conditioned PoliciesCode0
Learning to Infer User Interface Attributes from Images—0
A New Framework for Query Efficient Active Imitation Learning—0
Hierarchical Variational Imitation Learning of Control ProgramsCode0
Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data—0
One-Shot Imitation Filming of Human Motion Videos—0
Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach—0
Relational Mimic for Visual Adversarial Imitation Learning—0
To Follow or not to Follow: Selective Imitation Learning from Observations—0
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