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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 926–950 of 2122 papers

TitleStatusHype
Data augmentation for efficient learning from parametric experts—0
Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation—0
dARt Vinci: Egocentric Data Collection for Surgical Robot Learning at Scale—0
Aligning Agents like Large Language Models—0
HE-Drive: Human-Like End-to-End Driving with Vision Language Models—0
HATSUKI : An anime character like robot figure platform with anime-style expressions and imitation learning based action generation—0
CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation—0
Signs of Language: Embodied Sign Language Fingerspelling Acquisition from Demonstrations for Human-Robot Interaction—0
Imitation with Neural Density Models—0
Avoidance of Manual Labeling in Robotic Autonomous Navigation Through Multi-Sensory Semi-Supervised Learning—0
Imitator Learning: Achieve Out-of-the-Box Imitation Ability in Variable Environments—0
Imit Diff: Semantics Guided Diffusion Transformer with Dual Resolution Fusion for Imitation Learning—0
HACTS: a Human-As-Copilot Teleoperation System for Robot Learning—0
Habitat-Web: Learning Embodied Object-Search Strategies from Human Demonstrations at Scale—0
H2O: A Benchmark for Visual Human-human Object Handover Analysis—0
Cut-and-Approximate: 3D Shape Reconstruction from Planar Cross-sections with Deep Reinforcement Learning—0
Implicit and Explicit Commonsense for Multi-sentence Video Captioning—0
Avoidance Learning Using Observational Reinforcement Learning—0
Alibaba’s Submission for the WMT 2020 APE Shared Task: Improving Automatic Post-Editing with Pre-trained Conditional Cross-Lingual BERT—0
GymFG: A Framework with a Gym Interface for FlightGear—0
Improved Sample Complexity of Imitation Learning for Barrier Model Predictive Control—0
Improving Adversarial Text Generation by Modeling the Distant Future—0
Curriculum Offline Imitating Learning—0
Curriculum Learning and Imitation Learning for Model-free Control on Financial Time-series—0
Guided Meta-Policy Search—0
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