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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 401–425 of 2122 papers

TitleStatusHype
Imitating Language via Scalable Inverse Reinforcement Learning—0
Preference-Based Multi-Agent Reinforcement Learning: Data Coverage and Algorithmic Techniques—0
MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at ScaleCode2
FlowRetrieval: Flow-Guided Data Retrieval for Few-Shot Imitation LearningCode0
Reinforcement Learning without Human Feedback for Last Mile Fine-Tuning of Large Language Models—0
In-Context Imitation Learning via Next-Token PredictionCode2
Atari-GPT: Benchmarking Multimodal Large Language Models as Low-Level Policies in Atari Games—0
Re-Mix: Optimizing Data Mixtures for Large Scale Imitation LearningCode1
Automating Deformable Gasket Assembly—0
Pareto Inverse Reinforcement Learning for Diverse Expert Policy Generation—0
ACE: A Cross-Platform Visual-Exoskeletons System for Low-Cost Dexterous Teleoperation—0
Optimizing Interpretable Decision Tree Policies for Reinforcement LearningCode0
A Survey of Embodied Learning for Object-Centric Robotic ManipulationCode3
RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands—0
Markov Balance Satisfaction Improves Performance in Strictly Batch Offline Imitation Learning—0
A Comparison of Imitation Learning Algorithms for Bimanual Manipulation—0
Personalized Dynamic Difficulty Adjustment -- Imitation Learning Meets Reinforcement LearningCode0
Semi-Supervised One-Shot Imitation Learning—0
Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations—0
PLANRL: A Motion Planning and Imitation Learning Framework to Bootstrap Reinforcement Learning—0
Navigating the Human Maze: Real-Time Robot Pathfinding with Generative Imitation LearningCode0
Adversarial Safety-Critical Scenario Generation using Naturalistic Human Driving Priors—0
KOI: Accelerating Online Imitation Learning via Hybrid Key-state Guidance—0
CMR-Agent: Learning a Cross-Modal Agent for Iterative Image-to-Point Cloud Registration—0
ParkingE2E: Camera-based End-to-end Parking Network, from Images to PlanningCode4
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