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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 1231–1240 of 2122 papers

TitleStatusHype
Stabilized Likelihood-based Imitation Learning via Denoising Continuous Normalizing Flow—0
Stable Relay Learning Optimization Approach for Fast Power System Production Cost Minimization Simulation—0
Stage Conscious Attention Network (SCAN) : A Demonstration-Conditioned Policy for Few-Shot Imitation—0
STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descent—0
State Alignment-based Imitation Learning—0
State- and context-dependent robotic manipulation and grasping via uncertainty-aware imitation learning—0
State Aware Imitation Learning—0
State-Only Imitation Learning by Trajectory Distribution Matching—0
State-Only Imitation Learning for Dexterous Manipulation—0
State Representation Learning from Demonstration—0
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