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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 1621–1630 of 2122 papers

TitleStatusHype
Autoregressive Knowledge Distillation through Imitation LearningCode0
Toward the Fundamental Limits of Imitation Learning—0
Imitation Learning for Neural Network Autopilot in Fixed-Wing Unmanned Aerial Systems—0
Learn by Observation: Imitation Learning for Drone Patrolling from Videos of A Human Navigator—0
Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles—0
ADAIL: Adaptive Adversarial Imitation Learning—0
Online Adaptive Learning for Runtime Resource Management of Heterogeneous SoCs—0
Adversarial Imitation Learning via Random Search—0
Forward and inverse reinforcement learning sharing network weights and hyperparameters—0
Visual Imitation Made Easy—0
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