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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 901–950 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
Hindsight Generative Adversarial Imitation Learning—0
DataMIL: Selecting Data for Robot Imitation Learning with Datamodels—0
Imitation Learning from Observation through Optimal Transport—0
HILONet: Hierarchical Imitation Learning from Non-Aligned Observations—0
Imitation Learning from Pixel Observations for Continuous Control—0
Data-Efficient Learning from Human Interventions for Mobile Robots—0
Back to Reality for Imitation Learning—0
Hierarchical Reinforcement Learning for Multi-agent MOBA Game—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
Data-driven Traffic Simulation: A Comprehensive Review—0
Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving—0
Imitation Learning of Factored Multi-agent Reactive Models—0
Data-Driven Simulation of Ride-Hailing Services using Imitation and Reinforcement Learning—0
Imitation Learning of Robot Policies by Combining Language, Vision and Demonstration—0
Aligning Robot and Human Representations—0
ADAIL: Adaptive Adversarial Imitation Learning—0
Accelerating Self-Imitation Learning from Demonstrations via Policy Constraints and Q-Ensemble—0
Hierarchical Imitation Learning of Team Behavior from Heterogeneous Demonstrations—0
Hierarchical Imitation Learning for Stochastic Environments—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
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
Aligning Agents like Large Language Models—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
Imitation with Neural Density Models—0
Avoidance of Manual Labeling in Robotic Autonomous Navigation Through Multi-Sensory Semi-Supervised Learning—0
Imitator Learning: Achieve Out-of-the-Box Imitation Ability in Variable Environments—0
Imit Diff: Semantics Guided Diffusion Transformer with Dual Resolution Fusion for Imitation 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
H2O: A Benchmark for Visual Human-human Object Handover Analysis—0
Cut-and-Approximate: 3D Shape Reconstruction from Planar Cross-sections with Deep Reinforcement Learning—0
Implicit and Explicit Commonsense for Multi-sentence Video Captioning—0
Avoidance Learning Using Observational Reinforcement Learning—0
Alibaba’s Submission for the WMT 2020 APE Shared Task: Improving Automatic Post-Editing with Pre-trained Conditional Cross-Lingual BERT—0
GymFG: A Framework with a Gym Interface for FlightGear—0
Improved Sample Complexity of Imitation Learning for Barrier Model Predictive Control—0
Improving Adversarial Text Generation by Modeling the Distant Future—0
Curriculum Offline Imitating Learning—0
Curriculum Learning and Imitation Learning for Model-free Control on Financial Time-series—0
Guided Meta-Policy Search—0
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