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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 891–900 of 2122 papers

TitleStatusHype
Hierarchical Imitation Learning of Team Behavior from Heterogeneous Demonstrations—0
Hierarchical Imitation Learning for Stochastic Environments—0
Imitation Learning for Non-Autoregressive Neural Machine Translation—0
Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approach—0
Data Driven Aircraft Trajectory Prediction with Deep Imitation Learning—0
Hierarchical Imitation and Reinforcement Learning—0
Data augmentation for efficient learning from parametric experts—0
Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation—0
Imitation Learning from Imperfect Demonstration—0
dARt Vinci: Egocentric Data Collection for Surgical Robot Learning at Scale—0
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