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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 1351–1400 of 2122 papers

TitleStatusHype
Variable-Speed Teaching-Playback as Real-World Data Augmentation for Imitation Learning—0
Variational Adaptive Noise and Dropout towards Stable Recurrent Neural Networks—0
Goal-Conditioned Variational Autoencoder Trajectory Primitives with Continuous and Discrete Latent Codes—0
Variational Model-Based Imitation Learning in High-Dimensional Observation Spaces—0
VectorPainter: Advanced Stylized Vector Graphics Synthesis Using Stroke-Style Priors—0
Versatile Skill Control via Self-supervised Adversarial Imitation of Unlabeled Mixed Motions—0
Injective State-Image Mapping facilitates Visual Adversarial Imitation Learning—0
VILD: Variational Imitation Learning with Diverse-quality Demonstrations—0
VIN-NBV: A View Introspection Network for Next-Best-View Selection for Resource-Efficient 3D Reconstruction—0
Vision-and-Language Navigation Generative Pretrained Transformer—0
Vision-Language Foundation Models as Effective Robot Imitators—0
Visual Adversarial Imitation Learning using Variational Models—0
Visual Attention Prediction Improves Performance of Autonomous Drone Racing Agents—0
Visual Encoders for Data-Efficient Imitation Learning in Modern Video Games—0
Visual Hindsight Self-Imitation Learning for Interactive Navigation—0
Visual Imitation Learning with Calibrated Contrastive Representation—0
Towards Learning to Imitate from a Single Video Demonstration—0
Visual Imitation Learning with Recurrent Siamese Networks—0
Visual Imitation Made Easy—0
Visual Imitation with a Minimal Adversary—0
Visual Semantic Planning using Deep Successor Representations—0
Visuospatial Skill Learning for Robots—0
VITaL Pretraining: Visuo-Tactile Pretraining for Tactile and Non-Tactile Manipulation Policies—0
VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigation—0
Wasserstein Adversarial Imitation Learning—0
Watch and Match: Supercharging Imitation with Regularized Optimal Transport—0
Watch, Try, Learn: Meta-Learning from Demonstrations and Reward—0
Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards—0
WayEx: Waypoint Exploration using a Single Demonstration—0
Waypoint-Based Imitation Learning for Robotic Manipulation—0
WebWISE: Web Interface Control and Sequential Exploration with Large Language Models—0
Weighted Maximum Entropy Inverse Reinforcement Learning—0
What AIs are not Learning (and Why)—0
What data do we need for training an AV motion planner?—0
What is the Reward for Handwriting? -- Handwriting Generation by Imitation Learning—0
What Makes A Good Fisherman? Linear Regression under Self-Selection Bias—0
What Makes it Ok to Set a Fire? Iterative Self-distillation of Contexts and Rationales for Disambiguating Defeasible Social and Moral Situations—0
What Matters for Adversarial Imitation Learning?—0
What Matters for Batch Online Reinforcement Learning in Robotics?—0
What Matters in Learning from Large-Scale Datasets for Robot Manipulation—0
What Matters to Enhance Traffic Rule Compliance of Imitation Learning for End-to-End Autonomous Driving—0
What's the Move? Hybrid Imitation Learning via Salient Points—0
What Would pi* Do?: Imitation Learning via Off-Policy Reinforcement Learning—0
What Would the Expert do()?: Causal Imitation Learning—0
When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?—0
When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence—0
Optimal Passenger-Seeking Policies on E-hailing Platforms Using Markov Decision Process and Imitation Learning—0
WildLMa: Long Horizon Loco-Manipulation in the Wild—0
WoMAP: World Models For Embodied Open-Vocabulary Object Localization—0
World-aware Planning Narratives Enhance Large Vision-Language Model Planner—0
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