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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 1651–1675 of 2122 papers

TitleStatusHype
Multi-Action Dialog Policy Learning with Interactive Human Teaching—0
CLUZH at SIGMORPHON 2020 Shared Task on Multilingual Grapheme-to-Phoneme Conversion—0
An Imitation Learning Approach for Cache Replacement—0
Lipschitzness Is All You Need To Tame Off-policy Generative Adversarial Imitation LearningCode0
Strictly Batch Imitation Learning by Energy-based Distribution MatchingCode0
When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence—0
PICO: Primitive Imitation for COntrol—0
Modelling Agent Policies with Interpretable Imitation Learning—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
Reparameterized Variational Divergence Minimization for Stable Imitation—0
Active Imitation Learning from Multiple Non-Deterministic Teachers: Formulation, Challenges, and Algorithms—0
Self-Imitation Learning via Generalized Lower Bound Q-learning—0
PAC Bounds for Imitation and Model-based Batch Learning of Contextual Markov Decision Processes—0
Modeling Human Driving Behavior through Generative Adversarial Imitation Learning—0
Stealing Deep Reinforcement Learning Models for Fun and Profit—0
Primal Wasserstein Imitation LearningCode0
Explaining Autonomous Driving by Learning End-to-End Visual Attention—0
Cross-Domain Imitation Learning with a Dual Structure—0
NewtonianVAE: Proportional Control and Goal Identification from Pixels via Physical Latent Spaces—0
Imitative Non-Autoregressive Modeling for Trajectory Forecasting and Imputation—0
Predictive Modeling of Periodic Behavior for Human-Robot Symbiotic Walking—0
Automatic Discovery of Interpretable Planning StrategiesCode0
Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets—0
A Simple Imitation Learning Method via Contrastive Regularization—0
Data Driven Aircraft Trajectory Prediction with Deep Imitation Learning—0
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