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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 2001–2050 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
Imitating Language via Scalable Inverse Reinforcement Learning—0
Imitating Opponent to Win: Adversarial Policy Imitation Learning in Two-player Competitive Games—0
Imitating Past Successes can be Very Suboptimal—0
SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World—0
Imitating Task and Motion Planning with Visuomotor Transformers—0
Imitation-Based Active Camera Control with Deep Convolutional Neural Network—0
Imitation Bootstrapped Reinforcement Learning—0
Imitation by Predicting Observations—0
Imitation from Diverse Behaviors: Wasserstein Quality Diversity Imitation Learning with Single-Step Archive Exploration—0
Imitation Game: A Model-based and Imitation Learning Deep Reinforcement Learning Hybrid—0
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios—0
Imitation Learning Approach for AI Driving Olympics Trained on Real-world and Simulation Data Simultaneously—0
Imitation Learning as f-Divergence Minimization—0
Imitation Learning As State Matching via Differentiable Physics—0
A Survey of Imitation Learning Methods, Environments and Metrics—0
Imitation Learning based Alternative Multi-Agent Proximal Policy Optimization for Well-Formed Swarm-Oriented Pursuit Avoidance—0
Forward and inverse reinforcement learning sharing network weights and hyperparameters—0
Imitation Learning by Coaching—0
Imitation Learning Datasets: A Toolkit For Creating Datasets, Training Agents and Benchmarking—0
Imitation Learning for Adaptive Control of a Virtual Soft Exoglove—0
Imitation Learning for Autonomous Trajectory Learning of Robot Arms in Space—0
Imitation Learning for End to End Vehicle Longitudinal Control with Forward Camera—0
Imitation Learning for Fashion Style Based on Hierarchical Multimodal Representation—0
Imitation Learning for High Precision Peg-in-Hole Tasks—0
Imitation Learning for Human Pose Prediction—0
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