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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 1101–1150 of 2122 papers

TitleStatusHype
ARC -- Actor Residual Critic for Adversarial Imitation Learning—0
Transferable Reward Learning by Dynamics-Agnostic Discriminator Ensemble—0
TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous DrivingCode3
Minimax Optimal Online Imitation Learning via Replay EstimationCode0
Play it by Ear: Learning Skills amidst Occlusion through Audio-Visual Imitation Learning—0
TaSIL: Taylor Series Imitation LearningCode0
Reinforcement Learning for Branch-and-Bound Optimisation using Retrospective TrajectoriesCode1
Data augmentation for efficient learning from parametric experts—0
Chain of Thought Imitation with Procedure Cloning—0
Learning Energy Networks with Generalized Fenchel-Young Losses—0
IL-flOw: Imitation Learning from Observation using Normalizing Flows—0
Generalizing to New Tasks via One-Shot Compositional Subgoals—0
An Empirical Investigation of Representation Learning for ImitationCode1
RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation—0
Delayed Reinforcement Learning by Imitation—0
Diverse Imitation Learning via Self-Organizing Generative Models—0
Hitting time for Markov decision process—0
SKILL-IL: Disentangling Skill and Knowledge in Multitask Imitation Learning—0
What Makes A Good Fisherman? Linear Regression under Self-Selection Bias—0
Semi-Supervised Imitation Learning of Team Policies from Suboptimal Demonstrations—0
ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated CharactersCode3
KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics GradientsCode1
Learning Value Functions from Undirected State-only Experience—0
From One Hand to Multiple Hands: Imitation Learning for Dexterous Manipulation from Single-Camera Teleoperation—0
Task-Induced Representation Learning—0
Imitation Learning from Observations under Transition Model DisparityCode0
The Boltzmann Policy Distribution: Accounting for Systematic Suboptimality in Human ModelsCode1
Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research—0
Non-Parallel Text Style Transfer with Self-Parallel SupervisionCode0
Evaluating the Effectiveness of Corrective Demonstrations and a Low-Cost Sensor for Dexterous ManipulationCode0
Divide & Conquer Imitation LearningCode0
Understanding Game-Playing Agents with Natural Language AnnotationsCode0
What Matters in Language Conditioned Robotic Imitation Learning over Unstructured DataCode1
Causal Confusion and Reward Misidentification in Preference-Based Reward Learning—0
When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?—0
Habitat-Web: Learning Embodied Object-Search Strategies from Human Demonstrations at Scale—0
Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning—0
Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning—0
Learning to Drive by Watching YouTube Videos: Action-Conditioned Contrastive Policy PretrainingCode1
Learning Generalizable Dexterous Manipulation from Human Grasp Affordance—0
GAIL-PT: A Generic Intelligent Penetration Testing Framework with Generative Adversarial Imitation LearningCode1
Information-Theoretic Policy Learning from Partial Observations with Fully Informed Decision Makers—0
Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language GenerationCode0
Accelerating Federated Edge Learning via Topology Optimization—0
ReIL: A Framework for Reinforced Intervention-based Imitation Learning—0
Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation—0
Modular Adaptive Policy Selection for Multi-Task Imitation Learning through Task DivisionCode0
A Visual Navigation Perspective for Category-Level Object Pose EstimationCode1
Reshaping Robot Trajectories Using Natural Language Commands: A Study of Multi-Modal Data Alignment Using Transformers—0
Dexterous Imitation Made Easy: A Learning-Based Framework for Efficient Dexterous Manipulation—0
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