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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 951–1000 of 2122 papers

TitleStatusHype
Improving Generative Adversarial Imitation Learning with Non-expert Demonstrations—0
HATSUKI : An anime character like robot figure platform with anime-style expressions and imitation learning based action generation—0
Improving Learning from Demonstrations by Learning from Experience—0
Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback—0
CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation—0
Improving Retrospective Language Agents via Joint Policy Gradient Optimization—0
Improving Sequential Recommendation Consistency with Self-Supervised Imitation—0
Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight—0
Signs of Language: Embodied Sign Language Fingerspelling Acquisition from Demonstrations for Human-Robot Interaction—0
Avoidance of Manual Labeling in Robotic Autonomous Navigation Through Multi-Sensory Semi-Supervised Learning—0
Incremental Learning of Retrievable Skills For Efficient Continual Task Adaptation—0
Infer and Adapt: Bipedal Locomotion Reward Learning from Demonstrations via Inverse Reinforcement 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
Infinite-Horizon Differentiable Model Predictive Control—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
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
Initial State Interventions for Deconfounded Imitation Learning—0
GymFG: A Framework with a Gym Interface for FlightGear—0
Curriculum Offline Imitating Learning—0
Inspiration Learning through Preferences—0
Instant Policy: In-Context Imitation Learning via Graph Diffusion—0
Curriculum Learning and Imitation Learning for Model-free Control on Financial Time-series—0
Guided Meta-Policy Search—0
Guided Imitation of Task and Motion Planning—0
InterACT: Inter-dependency Aware Action Chunking with Hierarchical Attention Transformers for Bimanual Manipulation—0
INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps—0
Interactive Agent Modeling by Learning to Probe—0
Autoverse: An Evolvable Game Language for Learning Robust Embodied Agents—0
Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning—0
GRP Model for Sensorimotor Learning—0
Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation—0
Grounding Language Plans in Demonstrations Through Counterfactual Perturbations—0
Language Conditioned Imitation Learning over Unstructured Data—0
Curating Demonstrations using Online Experience—0
Interpretable Modeling of Deep Reinforcement Learning Driven Scheduling—0
Grasping with Chopsticks: Combating Covariate Shift in Model-free Imitation Learning for Fine Manipulation—0
IntervenGen: Interventional Data Generation for Robust and Data-Efficient Robot Imitation Learning—0
CubeDAgger: Improved Robustness of Interactive Imitation Learning without Violation of Dynamic Stability—0
Graph Neural Networks for Multi-Robot Active Information Acquisition—0
Graph Neural Networks for Decentralized Multi-Agent Perimeter Defense—0
CrowdPlay: Crowdsourcing human demonstration data for offline learning in Atari games—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
Inverse Factorized Q-Learning for Cooperative Multi-agent Imitation Learning—0
Inverse Rational Control: Inferring What You Think from How You Forage—0
A Graph-based Adversarial Imitation Learning Framework for Reliable & Realtime Fleet Scheduling in Urban Air Mobility—0
AdaDemo: Data-Efficient Demonstration Expansion for Generalist Robotic Agent—0
Accelerating Inverse Reinforcement Learning with Expert Bootstrapping—0
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