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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 701–725 of 2122 papers

TitleStatusHype
Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning—0
MimicGen: A Data Generation System for Scalable Robot Learning using Human DemonstrationsCode2
Model-Based Runtime Monitoring with Interactive Imitation Learning—0
MimicTouch: Leveraging Multi-modal Human Tactile Demonstrations for Contact-rich Manipulation—0
Data-driven Traffic Simulation: A Comprehensive Review—0
Good Better Best: Self-Motivated Imitation Learning for noisy Demonstrations—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
WebWISE: Web Interface Control and Sequential Exploration with Large Language Models—0
Promoting Generalization for Exact Solvers via Adversarial Instance Augmentation—0
Robust Visual Imitation Learning with Inverse Dynamics Representations—0
Learning Generalizable Manipulation Policies with Object-Centric 3D Representations—0
Learning to Discern: Imitating Heterogeneous Human Demonstrations with Preference and Representation Learning—0
LeTFuser: Light-weight End-to-end Transformer-Based Sensor Fusion for Autonomous Driving with Multi-Task LearningCode1
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
Towards Example-Based NMT with Multi-Levenshtein TransformersCode0
Generative Intrinsic Optimization: Intrinsic Control with Model Learning—0
Cross-Episodic Curriculum for Transformer Agents—0
Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning—0
RoboCLIP: One Demonstration is Enough to Learn Robot PoliciesCode1
Imitation Learning from Purified DemonstrationsCode0
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