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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 1501–1525 of 2122 papers

TitleStatusHype
A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning—0
A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation—0
Affordances from Human Videos as a Versatile Representation for Robotics—0
AgentMixer: Multi-Agent Correlated Policy Factorization—0
A Geometric Perspective on Visual Imitation Learning—0
AGIL: Learning Attention from Human for Visuomotor Tasks—0
A Graph-based Adversarial Imitation Learning Framework for Reliable & Realtime Fleet Scheduling in Urban Air Mobility—0
Alibaba’s Submission for the WMT 2020 APE Shared Task: Improving Automatic Post-Editing with Pre-trained Conditional Cross-Lingual BERT—0
Aligning Agents like Large Language Models—0
Aligning Robot and Human Representations—0
Align Your Intents: Offline Imitation Learning via Optimal Transport—0
A Linearly Constrained Nonparametric Framework for Imitation Learning—0
Task-Agnostic Learning to Accomplish New Tasks—0
AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control—0
A Model-Based Approach to Imitation Learning through Multi-Step Predictions—0
Amortized Noisy Channel Neural Machine Translation—0
Amortized nonmyopic active search via deep imitation learning—0
An Adaptive Human Driver Model for Realistic Race Car Simulations—0
An Algorithmic Perspective on Imitation Learning—0
Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach—0
An Analysis of Logit Learning with the r-Lambert Function—0
An Energy-Aware Online Learning Framework for Resource Management in Heterogeneous Platforms—0
A New Corpus and Imitation Learning Framework for Context-Dependent Semantic Parsing—0
A New Framework for Query Efficient Active Imitation Learning—0
A New Path: Scaling Vision-and-Language Navigation with Synthetic Instructions and Imitation Learning—0
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