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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 1201–1225 of 2122 papers

TitleStatusHype
Gesture2Path: Imitation Learning for Gesture-aware Navigation—0
Spatial-Temporal Deep Embedding for Vehicle Trajectory Reconstruction from High-Angle Video—0
Imitrob: Imitation Learning Dataset for Training and Evaluating 6D Object Pose EstimatorsCode0
Versatile Skill Control via Self-supervised Adversarial Imitation of Unlabeled Mixed Motions—0
Masked Imitation Learning: Discovering Environment-Invariant Modalities in Multimodal Demonstrations—0
CenterLineDet: CenterLine Graph Detection for Road Lanes with Vehicle-mounted Sensors by Transformer for HD Map Generation—0
Signs of Language: Embodied Sign Language Fingerspelling Acquisition from Demonstrations for Human-Robot Interaction—0
Task-Agnostic Learning to Accomplish New Tasks—0
Levenshtein OCRCode0
TarGF: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specification—0
Co-Imitation: Learning Design and Behaviour by Imitation—0
MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization—0
Weighted Maximum Entropy Inverse Reinforcement Learning—0
Learning to Structure an Image with Few Colors and Beyond—0
Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning—0
Sequential Causal Imitation Learning with Unobserved Confounders—0
Causal Imitation Learning with Unobserved Confounders—0
Exploring the trade off between human driving imitation and safety for traffic simulation—0
Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of MindCode0
Understanding Adversarial Imitation Learning in Small Sample Regime: A Stage-coupled Analysis—0
Sequence Model Imitation Learning with Unobserved ContextsCode0
See What the Robot Can't See: Learning Cooperative Perception for Visual NavigationCode0
Robot Policy Learning from Demonstration Using Advantage Weighting and Early Termination—0
Improved Policy Optimization for Online Imitation LearningCode0
Robots Enact Malignant Stereotypes—0
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