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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 801–850 of 2122 papers

TitleStatusHype
Hierarchical Reinforcement Learning for Multi-agent MOBA Game—0
Data-driven Traffic Simulation: A Comprehensive Review—0
HILONet: Hierarchical Imitation Learning from Non-Aligned Observations—0
Explaining Autonomous Driving by Learning End-to-End Visual Attention—0
Hindsight Generative Adversarial Imitation Learning—0
Hindsight is Only 50/50: Unsuitability of MDP based Approximate POMDP Solvers for Multi-resolution Information Gathering—0
Imitation Learning via Simultaneous Optimization of Policies and Auxiliary Trajectories—0
How Do We Move: Modeling Human Movement with System Dynamics—0
How hard is it to cross the room? -- Training (Recurrent) Neural Networks to steer a UAV—0
Explainable Hierarchical Imitation Learning for Robotic Drink Pouring—0
DDIL: Diversity Enhancing Diffusion Distillation With Imitation Learning—0
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples—0
How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontiers—0
How to Train Your Robots? The Impact of Demonstration Modality on Imitation Learning—0
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
Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration—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
Coarse-to-Fine 3D Keyframe Transporter—0
HybridGen: VLM-Guided Hybrid Planning for Scalable Data Generation of Imitation Learning—0
Imitation Learning from Pixel-Level Demonstrations by HashReward—0
Hybrid Imitation-Learning Motion Planner for Urban Driving—0
CNT (Conditioning on Noisy Targets): A new Algorithm for Leveraging Top-Down Feedback—0
Bayesian Multi-type Mean Field Multi-agent Imitation Learning—0
Deep Bayesian Reward Learning from Preferences—0
Hyperparameter Selection for Imitation Learning—0
Adversarial Imitation Learning via Random Search—0
I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation—0
Imitation Learning Datasets: A Toolkit For Creating Datasets, Training Agents and Benchmarking—0
Imitation Learning for End to End Vehicle Longitudinal Control with Forward Camera—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
ExACT: An End-to-End Autonomous Excavator System Using Action Chunking With Transformers—0
Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation—0
CNN-based Game State Detection for a Foosball Table—0
BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning—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
BEAC: Imitating Complex Exploration and Task-oriented Behaviors for Invisible Object Nonprehensile Manipulation—0
Imitate then Transcend: Multi-Agent Optimal Execution with Dual-Window Denoise PPO—0
Imitate TheWorld: A Search Engine Simulation Platform—0
Evolution of cooperation in the public goods game with Q-learning—0
Evolutionary Selective Imitation: Interpretable Agents by Imitation Learning Without a Demonstrator—0
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