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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 2001–2025 of 2122 papers

TitleStatusHype
Human2LocoMan: Learning Versatile Quadrupedal Manipulation with Human Pretraining—0
Human-Agent Cooperation in Bridge Bidding—0
Human AI interaction loop training: New approach for interactive reinforcement learning—0
Human-in-the-Loop Imitation Learning using Remote Teleoperation—0
Human-in-the-Loop Task and Motion Planning for Imitation Learning—0
Human-like Bots for Tactical Shooters Using Compute-Efficient Sensors—0
Human-Robot Navigation using Event-based Cameras and Reinforcement Learning—0
Hybrid Adversarial Imitation Learning—0
HybridGen: VLM-Guided Hybrid Planning for Scalable Data Generation of Imitation Learning—0
Hybrid Imitation Learning for Real-Time Service Restoration in Resilient Distribution Systems—0
Hybrid Imitation-Learning Motion Planner for Urban Driving—0
Hyperparameter Selection for Imitation Learning—0
I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation—0
IBCB: Efficient Inverse Batched Contextual Bandit for Behavioral Evolution History—0
Identifying Differential Patient Care Through Inverse Intent Inference—0
Identifying Selections for Unsupervised Subtask Discovery—0
IDIL: Imitation Learning of Intent-Driven Expert Behavior—0
ILAEDA: An Imitation Learning Based Approach for Automatic Exploratory Data Analysis—0
ILCAS: Imitation Learning-Based Configuration-Adaptive Streaming for Live Video Analytics with Cross-Camera Collaboration—0
IL-flOw: Imitation Learning from Observation using Normalizing Flows—0
IL-SOAR : Imitation Learning with Soft Optimistic Actor cRitic—0
Imitate then Transcend: Multi-Agent Optimal Execution with Dual-Window Denoise PPO—0
Imitate TheWorld: A Search Engine Simulation Platform—0
Imitating by generating: deep generative models for imitation of interactive tasks—0
Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning—0
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