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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 1776–1800 of 2122 papers

TitleStatusHype
Cross Domain Imitation Learning—0
Learning Effective Exploration Strategies For Contextual Bandits—0
Adaptive Adversarial Imitation Learning—0
Policy Optimization by Local Improvement through Search—0
Partial Simulation for Imitation Learning—0
Towards Scalable Imitation Learning for Multi-Agent Systems with Graph Neural Networks—0
MoET: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees—0
Learning to Reach Goals Without Reinforcement Learning—0
Self-Imitation Learning via Trajectory-Conditioned Policy for Hard-Exploration Tasks—0
Support-guided Adversarial Imitation Learning—0
Goal-Conditioned Video Prediction—0
ASYNCHRONOUS MULTI-AGENT GENERATIVE ADVERSARIAL IMITATION LEARNING—0
Accept Synthetic Objects as Real: End-to-End Training of Attentive Deep Visuomotor Policies for Manipulation in ClutterCode0
Avoidance Learning Using Observational Reinforcement Learning—0
Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic SupervisorCode0
Leveraging Human Guidance for Deep Reinforcement Learning Tasks—0
Safer End-to-End Autonomous Driving via Conditional Imitation Learning and Command Augmentation—0
Learning Your Way Without Map or Compass: Panoramic Target Driven Visual Navigation—0
Self-Supervised Correspondence in Visuomotor Policy LearningCode0
Learning Visuomotor Policies for Aerial Navigation Using Cross-Modal RepresentationsCode0
State Representation Learning from Demonstration—0
A Linearly Constrained Nonparametric Framework for Imitation Learning—0
VILD: Variational Imitation Learning with Diverse-quality Demonstrations—0
Deep attention networks reveal the rules of collective motion in zebrafishCode0
MPC-Net: A First Principles Guided Policy SearchCode0
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