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 776–800 of 2122 papers

TitleStatusHype
Guided Meta-Policy Search—0
Explaining Imitation Learning through Frames—0
Autoverse: An Evolvable Game Language for Learning Robust Embodied Agents—0
CodeDiffuser: Attention-Enhanced Diffusion Policy via VLM-Generated Code for Instruction Ambiguity—0
CMR-Agent: Learning a Cross-Modal Agent for Iterative Image-to-Point Cloud Registration—0
Curriculum Offline Imitating Learning—0
GymFG: A Framework with a Gym Interface for FlightGear—0
Hyperparameter Selection for Imitation Learning—0
Habitat-Web: Learning Embodied Object-Search Strategies from Human Demonstrations at Scale—0
Explaining Fast Improvement in Online Imitation Learning—0
Explaining Autonomous Driving by Learning End-to-End Visual Attention—0
Imitation Learning via Simultaneous Optimization of Policies and Auxiliary Trajectories—0
Signs of Language: Embodied Sign Language Fingerspelling Acquisition from Demonstrations for Human-Robot Interaction—0
HATSUKI : An anime character like robot figure platform with anime-style expressions and imitation learning based action generation—0
HE-Drive: Human-Like End-to-End Driving with Vision Language Models—0
Explainable Hierarchical Imitation Learning for Robotic Drink Pouring—0
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples—0
Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration—0
Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation—0
Coarse-to-Fine 3D Keyframe Transporter—0
Imitation Learning from Pixel-Level Demonstrations by HashReward—0
Hierarchical Imitation Learning for Stochastic Environments—0
Hierarchical Imitation Learning of Team Behavior from Heterogeneous Demonstrations—0
CNT (Conditioning on Noisy Targets): A new Algorithm for Leveraging Top-Down Feedback—0
Adversarial Imitation Learning via Random Search—0
Show:102550
← PrevPage 32 of 85Next →

No leaderboard results yet.