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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 1526–1550 of 2122 papers

TitleStatusHype
An Imitation Learning Approach for Cache Replacement—0
An Imitation Learning Based Algorithm Enabling Priori Knowledge Transfer in Modern Electricity Markets for Bayesian Nash Equilibrium Estimation—0
An Imitation Learning Curriculum for Text Editing with Non-Autoregressive Models—0
An Improved Reinforcement Learning Algorithm for Learning to Branch—0
An Integrated Imitation and Reinforcement Learning Methodology for Robust Agile Aircraft Control with Limited Pilot Demonstration Data—0
A Note on Sample Complexity of Interactive Imitation Learning with Log Loss—0
AnySkill: Learning Open-Vocabulary Physical Skill for Interactive Agents—0
AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System—0
A Perspective on AI-Guided Molecular Simulations in VR: Exploring Strategies for Imitation Learning in Hyperdimensional Molecular Systems—0
Application-Driven Relation Extraction with Limited Distant Supervision—0
Approximated Variational Bayesian Inverse Reinforcement Learning for Large Language Model Alignment—0
Approximate Dynamic Oracle for Dependency Parsing with Reinforcement Learning—0
Approximate Inverse Reinforcement Learning from Vision-based Imitation Learning—0
A Pragmatic Look at Deep Imitation Learning—0
A Probabilistic Framework for Imitating Human Race Driver Behavior—0
A Ranking Game for Imitation Learning—0
ARC -- Actor Residual Critic for Adversarial Imitation Learning—0
ARCap: Collecting High-quality Human Demonstrations for Robot Learning with Augmented Reality Feedback—0
Car-Following Models: A Multidisciplinary Review—0
Ark: An Open-source Python-based Framework for Robot Learning—0
ARMOR: Egocentric Perception for Humanoid Robot Collision Avoidance and Motion Planning—0
ArticuBot: Learning Universal Articulated Object Manipulation Policy via Large Scale Simulation—0
A Simple Imitation Learning Method via Contrastive Regularization—0
Asking Before Acting: Gather Information in Embodied Decision Making with Language Models—0
Asking for Help: Failure Prediction in Behavioral Cloning through Value Approximation—0
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