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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 1951–2000 of 2122 papers

TitleStatusHype
Learning Finite State Representations of Recurrent Policy Networks—0
Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation—0
Connecting the Dots Between MLE and RL for Sequence Prediction—0
Early Fusion for Goal Directed Robotic Vision—0
Guiding Policies with Language via Meta-LearningCode0
An Algorithmic Perspective on Imitation Learning—0
Reward learning from human preferences and demonstrations in AtariCode0
Learning to Compensate Photovoltaic Power Fluctuations from Images of the Sky by Imitating an Optimal Policy—0
Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation PredictionCode0
RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitation—0
Dynamic Regret Convergence Analysis and an Adaptive Regularization Algorithm for On-Policy Robot Imitation LearningCode0
Learning to Defend by Learning to Attack—0
Approximate Dynamic Oracle for Dependency Parsing with Reinforcement Learning—0
Learning Beam Search Policies via Imitation LearningCode0
Navigation by Imitation in a Pedestrian-Rich Environment—0
Differentiable MPC for End-to-end Planning and ControlCode0
Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-TimeCode0
Neural Modular Control for Embodied Question AnsweringCode0
One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks—0
Predictor-Corrector Policy OptimizationCode0
Deep Imitative Models for Flexible Inference, Planning, and ControlCode0
Task-Embedded Control Networks for Few-Shot Imitation LearningCode0
Task-Oriented Hand Motion Retargeting for Dexterous Manipulation ImitationCode0
Injective State-Image Mapping facilitates Visual Adversarial Imitation Learning—0
Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information FlowCode0
Interactive Agent Modeling by Learning to Probe—0
UZH at CoNLL--SIGMORPHON 2018 Shared Task on Universal Morphological Reinflection—0
Learning to Actively Learn Neural Machine Translation—0
Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information—0
Visual Imitation Learning with Recurrent Siamese Networks—0
What Would pi* Do?: Imitation Learning via Off-Policy Reinforcement Learning—0
Mimicking actions is a good strategy for beginners: Fast Reinforcement Learning with Expert Action Sequences—0
Improving Generative Adversarial Imitation Learning with Non-expert Demonstrations—0
Inspiration Learning through Preferences—0
Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation LearningCode0
Sample-Efficient Imitation Learning via Generative Adversarial NetsCode0
3D Ego-Pose Estimation via Imitation Learning—0
Imitation Learning for Neural Morphological String TransductionCode0
Shared Multi-Task Imitation Learning for Indoor Self-Navigation—0
Risk-Sensitive Generative Adversarial Imitation Learning—0
EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning—0
Generative Adversarial Imitation from ObservationCode0
Bipedal Walking Robot using Deep Deterministic Policy GradientCode0
Extracting Contact and Motion from Manipulation Videos—0
CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving—0
Universal Planning Networks: Learning Generalizable Representations for Visuomotor ControlCode0
Learning How to Actively Learn: A Deep Imitation Learning ApproachCode0
End-to-End Deep Imitation Learning: Robot Soccer Case Study—0
The Virtuous Machine - Old Ethics for New Technology?—0
Learning Existing Social Conventions via Observationally Augmented Self-Play—0
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