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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 1101–1125 of 2122 papers

TitleStatusHype
Automated Task-Time Interventions to Improve Teamwork using Imitation Learning—0
Diffusion Model-Augmented Behavioral Cloning—0
Simulation of robot swarms for learning communication-aware coordinationCode0
K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs—0
CILP: Co-simulation based Imitation Learner for Dynamic Resource Provisioning in Cloud Computing EnvironmentsCode0
Robust Question Answering against Distribution Shifts with Test-Time Adaptation: An Empirical StudyCode0
Asking for Help: Failure Prediction in Behavioral Cloning through Value Approximation—0
Scaling Vision-based End-to-End Driving with Multi-View Attention Learning—0
Fine-grained Affordance Annotation for Egocentric Hand-Object Interaction VideosCode0
Target-based Surrogates for Stochastic OptimizationCode0
A Strong Baseline for Batch Imitation Learning—0
DITTO: Offline Imitation Learning with World Models—0
Aligning Robot and Human Representations—0
Synthesizing Physical Character-Scene Interactions—0
Superhuman FairnessCode0
Hierarchical Imitation Learning with Vector Quantized ModelsCode0
Optimal Decision Tree Policies for Markov Decision ProcessesCode0
Identifying Expert Behavior in Offline Training Datasets Improves Behavioral Cloning of Robotic Manipulation PoliciesCode0
Improving Behavioural Cloning with Positive Unlabeled Learning—0
Theoretical Analysis of Offline Imitation With Supplementary DatasetCode0
SMART: Self-supervised Multi-task pretrAining with contRol Transformers—0
Language-guided Task Adaptation for Imitation Learning—0
Graph Neural Networks for Decentralized Multi-Agent Perimeter Defense—0
Domain-adapted Learning and Imitation: DRL for Power Arbitrage—0
DIRECT: Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training—0
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