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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 1551–1575 of 2122 papers

TitleStatusHype
Learning from Demonstrations with Energy based Generative Adversarial Imitation Learning—0
Learning Efficient Planning-based Rewards for Imitation Learning—0
Goal-Driven Imitation Learning from Observation by Inferring Goal Proximity—0
PERIL: Probabilistic Embeddings for hybrid Meta-Reinforcement and Imitation Learning—0
Robust Asymmetric Learning in POMDPsCode0
Synthesizing Decentralized Controllers with Graph Neural Networks and Imitation Learning—0
Multi-Instance Aware Localization for End-to-End Imitation Learning—0
Translating Natural Language Instructions to Computer Programs for Robot Manipulation—0
Stochastic Action Prediction for Imitation Learning—0
Imitation Learning for High Precision Peg-in-Hole Tasks—0
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II—0
Rethink AI-based Power Grid Control: Diving Into Algorithm Design—0
Self-Imitation Advantage Learning—0
myGym: Modular Toolkit for Visuomotor Robotic Tasks—0
Active Hierarchical Imitation and Reinforcement Learning—0
Learn to Play Tetris with Deep Reinforcement Learning—0
Using Enhanced Gaussian Cross-Entropy in Imitation Learning to Digging the First Diamond in Minecraft—0
Learning Multi-Arm Manipulation Through Collaborative Teleoperation—0
Human-in-the-Loop Imitation Learning using Remote Teleoperation—0
Imitation-Based Active Camera Control with Deep Convolutional Neural Network—0
Flatland-RL : Multi-Agent Reinforcement Learning on Trains—0
Neural Rate Control for Video Encoding using Imitation Learning—0
Selective Eye-gaze Augmentation To Enhance Imitation Learning In Atari Games—0
Neural Dynamic Policies for End-to-End Sensorimotor Learning—0
MILP-based Imitation Learning for HVAC control—0
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