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 1951–1975 of 2122 papers

TitleStatusHype
Generative predecessor models for sample-efficient imitation learning—0
Generic Oracles for Structured Prediction—0
Genetic Imitation Learning by Reward Extrapolation—0
GenH2R: Learning Generalizable Human-to-Robot Handover via Scalable Simulation, Demonstration, and Imitation—0
GenH2R: Learning Generalizable Human-to-Robot Handover via Scalable Simulation Demonstration and Imitation—0
GenOSIL: Generalized Optimal and Safe Robot Control using Parameter-Conditioned Imitation Learning—0
Gesture2Path: Imitation Learning for Gesture-aware Navigation—0
Get Back Here: Robust Imitation by Return-to-Distribution Planning—0
GHIL-Glue: Hierarchical Control with Filtered Subgoal Images—0
Global overview of Imitation Learning—0
Global Reinforcement Learning: Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods—0
Goal-conditioned Imitation Learning—0
Goal-Conditioned Video Prediction—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
Good Better Best: Self-Motivated Imitation Learning for noisy Demonstrations—0
Good Data Is All Imitation Learning Needs—0
GR00T N1: An Open Foundation Model for Generalist Humanoid Robots—0
Graph-based Prediction and Planning Policy Network (GP3Net) for scalable self-driving in dynamic environments using Deep Reinforcement Learning—0
Graph Neural Network Policies and Imitation Learning for Multi-Domain Task-Oriented Dialogues—0
Graph Neural Networks for Decentralized Multi-Agent Perimeter Defense—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
Language Conditioned Imitation Learning over Unstructured Data—0
Grounding Language Plans in Demonstrations Through Counterfactual Perturbations—0
Show:102550
← PrevPage 79 of 85Next →

No leaderboard results yet.