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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 1001–1025 of 2122 papers

TitleStatusHype
Waypoint-Based Imitation Learning for Robotic Manipulation—0
Deep Homography Prediction for Endoscopic Camera Motion Imitation LearningCode0
Contextual Bandits and Imitation Learning via Preference-Based Active Queries—0
Offline Diversity Maximization Under Imitation Constraints—0
On Combining Expert Demonstrations in Imitation Learning via Optimal Transport—0
Multi-Stage Cable Routing through Hierarchical Imitation Learning—0
Improving End-to-End Speech Translation by Imitation-Based Knowledge Distillation with Synthetic TranscriptsCode0
Selective Sampling and Imitation Learning via Online Regression—0
AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System—0
SpawnNet: Learning Generalizable Visuomotor Skills from Pre-trained Networks—0
Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulation—0
Policy Contrastive Imitation Learning—0
RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot—0
RObotic MAnipulation Network (ROMAN) x2013 Hybrid Hierarchical Learning for Solving Complex Sequential Tasks—0
IIFL: Implicit Interactive Fleet Learning from Heterogeneous Human SupervisorsCode0
Learning Dynamic Graph for Overtaking Strategy in Autonomous Driving—0
Learning non-Markovian Decision-Making from State-only SequencesCode0
On Imitation in Mean-field Games—0
CEIL: Generalized Contextual Imitation Learning—0
Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching—0
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy ImitationCode0
CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning—0
One-shot Imitation Learning via Interaction Warping—0
SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models—0
Active Policy Improvement from Multiple Black-box OraclesCode0
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