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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 976–1000 of 2122 papers

TitleStatusHype
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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