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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 1376–1400 of 2122 papers

TitleStatusHype
Learning Robotic Ultrasound Scanning Skills via Human Demonstrations and Guided Explorations—0
Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner—0
TRAIL: Near-Optimal Imitation Learning with Suboptimal Data—0
Towards More Generalizable One-shot Visual Imitation Learning—0
Efficient Robotic Manipulation Through Offline-to-Online Reinforcement Learning and Goal-Aware State Information—0
Periodic DMP formulation for Quaternion Trajectories—0
Continuous Control with Action Quantization from Demonstrations—0
SS-MAIL: Self-Supervised Multi-Agent Imitation Learning—0
Generative Adversarial Imitation Learning for End-to-End Autonomous Driving on Urban Environments—0
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning—0
Reinforcement Learning In Two Player Zero Sum Simultaneous Action GamesCode0
Leveraging Experience in Lazy Search—0
Safe Imitation Learning on Real-Life Highway Data for Human-like Autonomous Driving—0
Goal-Directed Design Agents: Integrating Visual Imitation with One-Step Lookahead Optimization for Generative Design—0
Procedure Planning in Instructional Videos via Contextual Modeling and Model-based Policy Learning—0
A Critique of Strictly Batch Imitation Learning—0
Deep Homography Estimation in Dynamic Surgical Scenes for Laparoscopic Camera Motion ExtractionCode0
Distributional Decision Transformer for Hindsight Information Matching—0
Mitigation of Adversarial Policy Imitation via Constrained Randomization of Policy (CRoP)—0
Fast fixed-backbone protein sequence and rotamer design—0
Meta-Imitation Learning by Watching Video Demonstrations—0
Multi-batch Reinforcement Learning via Sample Transfer and Imitation Learning—0
State-Only Imitation Learning by Trajectory Distribution Matching—0
Fight fire with fire: countering bad shortcuts in imitation learning with good shortcuts—0
Stabilized Likelihood-based Imitation Learning via Denoising Continuous Normalizing Flow—0
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