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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 901–925 of 2122 papers

TitleStatusHype
Imitation Learning from Nonlinear MPC via the Exact Q-Loss and its Gauss-Newton Approximation—0
Imitation Learning from Observations: An Autoregressive Mixture of Experts Approach—0
Accelerating Self-Imitation Learning from Demonstrations via Policy Constraints and Q-Ensemble—0
Hierarchical Imitation Learning of Team Behavior from Heterogeneous Demonstrations—0
Imitation Learning from Observation through Optimal Transport—0
Hierarchical Imitation Learning for Stochastic Environments—0
Imitation Learning from Pixel Observations for Continuous Control—0
Data Driven Aircraft Trajectory Prediction with Deep Imitation Learning—0
Hierarchical Imitation and Reinforcement Learning—0
Data augmentation for efficient learning from parametric experts—0
Imitation Learning from Visual Data with Multiple Intentions—0
Imitation Learning in Discounted Linear MDPs without exploration assumptions—0
Imitation Learning Inputting Image Feature to Each Layer of Neural Network—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
Imitation Learning of Factored Multi-agent Reactive Models—0
Aligning Agents like Large Language Models—0
Imitation Learning of Robot Policies by Combining Language, Vision and Demonstration—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
Avoidance of Manual Labeling in Robotic Autonomous Navigation Through Multi-Sensory Semi-Supervised 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
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