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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 1326–1350 of 2122 papers

TitleStatusHype
ImitAL: Learning Active Learning Strategies from Synthetic DataCode0
DexMV: Imitation Learning for Dexterous Manipulation from Human VideosCode1
DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks—0
Imitation Learning by Reinforcement LearningCode0
Towards real-world navigation with deep differentiable plannersCode1
iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household TasksCode1
What Matters in Learning from Offline Human Demonstrations for Robot ManipulationCode2
A Pragmatic Look at Deep Imitation Learning—0
Adaptive t-Momentum-based Optimization for Unknown Ratio of Outliers in Amateur Data in Imitation Learning—0
Self-Supervised Disentangled Representation Learning for Third-Person Imitation Learning—0
CLUZH at SIGMORPHON 2021 Shared Task on Multilingual Grapheme-to-Phoneme Conversion: Variations on a Baseline—0
Meta-Reinforcement Learning for Mastering Multiple Skills and Generalizing across Environments in Text-based Games—0
Generic Oracles for Structured Prediction—0
Transformer-based deep imitation learning for dual-arm robot manipulation—0
Brain-Inspired Deep Imitation Learning for Autonomous Driving SystemsCode0
Reinforced Imitation Learning by Free Energy Principle—0
Training Electric Vehicle Charging Controllers with Imitation Learning—0
Learning a Large Neighborhood Search Algorithm for Mixed Integer ProgramsCode1
Critic Guided Segmentation of Rewarding Objects in First-Person ViewsCode1
Playful Interactions for Representation Learning—0
Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement LearningCode1
Visual Adversarial Imitation Learning using Variational Models—0
Imitate TheWorld: A Search Engine Simulation Platform—0
Generating stable molecules using imitation and reinforcement learning—0
Multi-Agent Imitation Learning with Copulas—0
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