SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1701–1725 of 2122 papers

TitleStatusHype
Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation—0
Curriculum Learning and Imitation Learning for Model-free Control on Financial Time-series—0
Curriculum Offline Imitating Learning—0
Cut-and-Approximate: 3D Shape Reconstruction from Planar Cross-sections with Deep Reinforcement Learning—0
CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation—0
dARt Vinci: Egocentric Data Collection for Surgical Robot Learning at Scale—0
Data augmentation for efficient learning from parametric experts—0
Data Driven Aircraft Trajectory Prediction with Deep Imitation Learning—0
Data-Driven Simulation of Ride-Hailing Services using Imitation and Reinforcement Learning—0
Data-driven Traffic Simulation: A Comprehensive Review—0
Data-Efficient Learning from Human Interventions for Mobile Robots—0
DataMIL: Selecting Data for Robot Imitation Learning with Datamodels—0
Data Quality in Imitation Learning—0
D-CODA: Diffusion for Coordinated Dual-Arm Data Augmentation—0
DDIL: Diversity Enhancing Diffusion Distillation With Imitation Learning—0
DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation—0
Synthesizing Decentralized Controllers with Graph Neural Networks and Imitation Learning—0
Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulation—0
Decoupling Skill Learning from Robotic Control for Generalizable Object Manipulation—0
Deep Bayesian Reward Learning from Preferences—0
Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations—0
Deep imitation learning for molecular inverse problems—0
Deep Learning for Visual Navigation of Underwater Robots—0
Deep Learning of Robotic Tasks without a Simulator using Strong and Weak Human Supervision—0
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction—0
Show:102550
← PrevPage 69 of 85Next →

No leaderboard results yet.