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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
Imitation Bootstrapped Reinforcement Learning—0
LOTUS: Continual Imitation Learning for Robot Manipulation Through Unsupervised Skill Discovery—0
Learning Realistic Traffic Agents in Closed-loop—0
A Statistical Guarantee for Representation Transfer in Multitask Imitation Learning—0
Vision-Language Foundation Models as Effective Robot Imitators—0
Enhanced Generalization through Prioritization and Diversity in Self-Imitation Reinforcement Learning over Procedural Environments with Sparse Rewards—0
Addressing Limitations of State-Aware Imitation Learning for Autonomous Driving—0
Deep Learning for Visual Navigation of Underwater Robots—0
Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning—0
Model-Based Runtime Monitoring with Interactive Imitation Learning—0
MimicTouch: Leveraging Multi-modal Human Tactile Demonstrations for Contact-rich Manipulation—0
What Makes it Ok to Set a Fire? Iterative Self-distillation of Contexts and Rationales for Disambiguating Defeasible Social and Moral Situations—0
Human-in-the-Loop Task and Motion Planning for Imitation Learning—0
Data-driven Traffic Simulation: A Comprehensive Review—0
Good Better Best: Self-Motivated Imitation Learning for noisy Demonstrations—0
WebWISE: Web Interface Control and Sequential Exploration with Large Language Models—0
Robust Visual Imitation Learning with Inverse Dynamics Representations—0
Learning Generalizable Manipulation Policies with Object-Centric 3D Representations—0
Promoting Generalization for Exact Solvers via Adversarial Instance Augmentation—0
Learning to Discern: Imitating Heterogeneous Human Demonstrations with Preference and Representation Learning—0
Few-Shot In-Context Imitation Learning via Implicit Graph Alignment—0
One-Shot Imitation Learning: A Pose Estimation Perspective—0
Efficient Online Learning with Offline Datasets for Infinite Horizon MDPs: A Bayesian Approach—0
Mimicking the Maestro: Exploring the Efficacy of a Virtual AI Teacher in Fine Motor Skill Acquisition—0
Progressively Efficient Learning—0
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