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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 751–775 of 2122 papers

TitleStatusHype
A Statistical Guarantee for Representation Transfer in Multitask Imitation Learning—0
Action-Free Reasoning for Policy Generalization—0
Cross Domain Imitation Learning—0
Goal-conditioned Imitation Learning—0
Exploration Based Language Learning for Text-Based Games—0
Explaining Imitation Learning through Frames—0
Goal-Directed Design Agents: Integrating Visual Imitation with One-Step Lookahead Optimization for Generative Design—0
Goal-Driven Imitation Learning from Observation by Inferring Goal Proximity—0
Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Cars—0
AGIL: Learning Attention from Human for Visuomotor Tasks—0
CodeDiffuser: Attention-Enhanced Diffusion Policy via VLM-Generated Code for Instruction Ambiguity—0
Good Better Best: Self-Motivated Imitation Learning for noisy Demonstrations—0
CMR-Agent: Learning a Cross-Modal Agent for Iterative Image-to-Point Cloud Registration—0
GR00T N1: An Open Foundation Model for Generalist Humanoid Robots—0
How hard is it to cross the room? -- Training (Recurrent) Neural Networks to steer a UAV—0
Graph Neural Network Policies and Imitation Learning for Multi-Domain Task-Oriented Dialogues—0
Explaining Fast Improvement in Online Imitation Learning—0
Graph Neural Networks for Multi-Robot Active Information Acquisition—0
Grasping with Chopsticks: Combating Covariate Shift in Model-free Imitation Learning for Fine Manipulation—0
Explaining Autonomous Driving by Learning End-to-End Visual Attention—0
Language Conditioned Imitation Learning over Unstructured Data—0
Grounding Language Plans in Demonstrations Through Counterfactual Perturbations—0
GRP Model for Sensorimotor Learning—0
Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning—0
Imitation Learning via Simultaneous Optimization of Policies and Auxiliary Trajectories—0
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