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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 451–475 of 2122 papers

TitleStatusHype
EC-SAGINs: Edge Computing-enhanced Space-Air-Ground Integrated Networks for Internet of Vehicles—0
DexGraspVLA: A Vision-Language-Action Framework Towards General Dexterous Grasping—0
DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning—0
Deep Bayesian Reward Learning from Preferences—0
DexterityGen: Foundation Controller for Unprecedented Dexterity—0
Dexterous Imitation Made Easy: A Learning-Based Framework for Efficient Dexterous Manipulation—0
Dexterous Manipulation through Imitation Learning: A Survey—0
Bayesian Multi-type Mean Field Multi-agent Imitation Learning—0
A Linearly Constrained Nonparametric Framework for Imitation Learning—0
DIAL: Distribution-Informed Adaptive Learning of Multi-Task Constraints for Safety-Critical Systems—0
Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach—0
Decoupling Skill Learning from Robotic Control for Generalizable Object Manipulation—0
Bayesian Learning for Dynamic Inference—0
Dyna-AIL : Adversarial Imitation Learning by Planning—0
Differentiable Constrained Imitation Learning for Robot Motion Planning and Control—0
An Analysis of Logit Learning with the r-Lambert Function—0
Differentiable Robust LQR Layers—0
DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching—0
Bayesian Imitation Learning for End-to-End Mobile Manipulation—0
Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulation—0
AdaManip: Adaptive Articulated Object Manipulation Environments and Policy Learning—0
Batch Recurrent Q-Learning for Backchannel Generation Towards Engaging Agents—0
Diffusion Imitation from Observation—0
Diffusion Meets DAgger: Supercharging Eye-in-hand Imitation Learning—0
Synthesizing Decentralized Controllers with Graph Neural Networks and Imitation Learning—0
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