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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 1831–1840 of 2122 papers

TitleStatusHype
On-Policy Robot Imitation Learning from a Converging Supervisor—0
Learning a Behavioral Repertoire from Demonstrations—0
Interactive-Predictive Neural Machine Translation through Reinforcement and Imitation—0
Co-training for Policy LearningCode0
Integration of Imitation Learning using GAIL and Reinforcement Learning using Task-achievement Rewards via Probabilistic Graphical Model—0
Active Learning within Constrained Environments through Imitation of an Expert Questioner—0
Sample Efficient Learning of Path Following and Obstacle Avoidance Behavior for Quadrotors—0
Supervise Thyself: Examining Self-Supervised Representations in Interactive EnvironmentsCode0
PyRep: Bringing V-REP to Deep Robot LearningCode0
Learning to Interactively Learn and Assist—0
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