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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 881–890 of 2122 papers

TitleStatusHype
Imitation Learning for Adaptive Control of a Virtual Soft Exoglove—0
ADAIL: Adaptive Adversarial Imitation Learning—0
Imitation Learning for Autonomous Trajectory Learning of Robot Arms in Space—0
Imitation Learning for End to End Vehicle Longitudinal Control with Forward Camera—0
Accelerating Self-Imitation Learning from Demonstrations via Policy Constraints and Q-Ensemble—0
Hierarchical Imitation Learning of Team Behavior from Heterogeneous Demonstrations—0
Hierarchical Imitation Learning for Stochastic Environments—0
Imitation Learning for Human Pose Prediction—0
Data Driven Aircraft Trajectory Prediction with Deep Imitation Learning—0
Hierarchical Imitation and Reinforcement Learning—0
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