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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 876–900 of 2122 papers

TitleStatusHype
Imitation Learning by Coaching—0
Event Extraction with Generative Adversarial Imitation Learning—0
Evaluation metrics for behaviour modeling—0
CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning—0
Asking Before Acting: Gather Information in Embodied Decision Making with Language Models—0
Imitation Learning for Adaptive Control of a Virtual Soft Exoglove—0
Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning—0
Imitation Learning for Autonomous Trajectory Learning of Robot Arms in Space—0
Imitation Learning for End to End Vehicle Longitudinal Control with Forward Camera—0
Adversarial Imitation Learning via Boosting—0
Imitation Learning for Fashion Style Based on Hierarchical Multimodal Representation—0
Imitation Learning for High Precision Peg-in-Hole Tasks—0
Imitation Learning for Human Pose Prediction—0
Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding—0
Imitation learning for language generation from unaligned data—0
Evaluation Function Approximation for Scrabble—0
Imitation Learning for Neural Network Autopilot in Fixed-Wing Unmanned Aerial Systems—0
Imitation Learning for Non-Autoregressive Neural Machine Translation—0
Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approach—0
Cloud-Based Hierarchical Imitation Learning for Scalable Transfer of Construction Skills from Human Workers to Assisting Robots—0
Imitation learning for structured prediction in natural language processing—0
Imitation Learning for Vision-based Lane Keeping Assistance—0
Deterministic Policy Imitation Gradient Algorithm—0
Imitation Learning from Imperfect Demonstration—0
A Simple Imitation Learning Method via Contrastive Regularization—0
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