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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 1001–1025 of 2122 papers

TitleStatusHype
A Graph-based Adversarial Imitation Learning Framework for Reliable & Realtime Fleet Scheduling in Urban Air Mobility—0
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?—0
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies—0
Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning—0
Hitting time for Markov decision process—0
AdaDemo: Data-Efficient Demonstration Expansion for Generalist Robotic Agent—0
Accelerating Inverse Reinforcement Learning with Expert Bootstrapping—0
Perceptual Motor Learning with Active Inference Framework for Robust Lateral Control—0
VITAL: Interactive Few-Shot Imitation Learning via Visual Human-in-the-Loop Corrections—0
JARVIS-VLA: Post-Training Large-Scale Vision Language Models to Play Visual Games with Keyboards and Mouse—0
Leveraging Human Guidance for Deep Reinforcement Learning Tasks—0
Graph Neural Network Policies and Imitation Learning for Multi-Domain Task-Oriented Dialogues—0
Kaiwu: A Multimodal Manipulation Dataset and Framework for Robot Learning and Human-Robot Interaction—0
Keyframe-Focused Visual Imitation Learning—0
Keypoint Abstraction using Large Models for Object-Relative Imitation Learning—0
Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics—0
Graph-based Prediction and Planning Policy Network (GP3Net) for scalable self-driving in dynamic environments using Deep Reinforcement Learning—0
GR00T N1: An Open Foundation Model for Generalist Humanoid Robots—0
KinTwin: Imitation Learning with Torque and Muscle Driven Biomechanical Models Enables Precise Replication of Able-Bodied and Impaired Movement from Markerless Motion Capture—0
Knowledge Distillation for Mobile Edge Computation Offloading—0
Good Data Is All Imitation Learning Needs—0
Lagrangian Generative Adversarial Imitation Learning with Safety—0
Crossing the Human-Robot Embodiment Gap with Sim-to-Real RL using One Human Demonstration—0
Good Better Best: Self-Motivated Imitation Learning for noisy Demonstrations—0
Cross-Episodic Curriculum for Transformer Agents—0
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