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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 1601–1650 of 2122 papers

TitleStatusHype
Explaining Fast Improvement in Online Imitation Learning—0
Guiding Deep Molecular Optimization with Genetic ExplorationCode1
Multi-Action Dialog Policy Learning with Interactive Human Teaching—0
Policy Improvement via Imitation of Multiple Oracles—0
Reinforcement Learning based Control of Imitative Policies for Near-Accident DrivingCode1
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
Intrinsic Reward Driven Imitation Learning via Generative ModelCode1
Strictly Batch Imitation Learning by Energy-based Distribution MatchingCode0
When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence—0
Adversarial Soft Advantage Fitting: Imitation Learning without Policy OptimizationCode1
Aligning Time Series on Incomparable SpacesCode1
PICO: Primitive Imitation for COntrol—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
Modelling Agent Policies with Interpretable 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
Wasserstein Distance guided Adversarial Imitation Learning with Reward Shape ExplorationCode1
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
Active Imitation Learning with Noisy GuidanceCode1
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
Language Conditioned Imitation Learning over Unstructured Data—0
BabyWalk: Going Farther in Vision-and-Language Navigation by Taking Baby StepsCode1
Probabilistic End-to-End Vehicle Navigation in Complex Dynamic Environments with Multimodal Sensor Fusion—0
Improving Adversarial Text Generation by Modeling the Distant Future—0
Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation—0
Off-Policy Adversarial Inverse Reinforcement LearningCode1
An Imitation Game for Learning Semantic Parsers from User InteractionCode1
Disagreement-Regularized Imitation LearningCode1
Synthesizing Programmatic Policies that Inductively Generalize—0
Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards—0
Towards Embodied Scene Description—0
Informed Sampling for Diversity in Concept-to-Text NLG—0
Augmented Behavioral Cloning from ObservationCode1
VTGNet: A Vision-based Trajectory Generation Network for Autonomous Vehicles in Urban EnvironmentsCode1
GymFG: A Framework with a Gym Interface for FlightGear—0
Learning Constrained Adaptive Differentiable Predictive Control Policies With GuaranteesCode1
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