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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 1426–1450 of 2122 papers

TitleStatusHype
Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation LearningCode0
Fixing exposure bias with imitation learning needs powerful oracles—0
Dynamic Modeling of Hand-Object Interactions via Tactile Sensing—0
Reinforcement Learning for Battery Energy Storage Dispatch augmented with Model-based Optimizer—0
Error-based or target-based? A unifying framework for learning in recurrent spiking networks—0
MimicBot: Combining Imitation and Reinforcement Learning to win in Bot Bowl—0
Provably Efficient Generative Adversarial Imitation Learning for Online and Offline Setting with Linear Function Approximation—0
ImitAL: Learning Active Learning Strategies from Synthetic DataCode0
DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks—0
Imitation Learning by Reinforcement LearningCode0
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
Transformer-based deep imitation learning for dual-arm robot manipulation—0
Generic Oracles for Structured Prediction—0
Meta-Reinforcement Learning for Mastering Multiple Skills and Generalizing across Environments in Text-based Games—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
Playful Interactions for Representation Learning—0
Imitate TheWorld: A Search Engine Simulation Platform—0
Visual Adversarial Imitation Learning using Variational Models—0
Generating stable molecules using imitation and reinforcement learning—0
Multi-Agent Imitation Learning with Copulas—0
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