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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 351–400 of 2122 papers

TitleStatusHype
Play to the Score: Stage-Guided Dynamic Multi-Sensory Fusion for Robotic Manipulation—0
Diffusion-Based Imitation Learning for Social Pose Generation—0
Addressing Limitations of State-Aware Imitation Learning for Autonomous Driving—0
Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulation—0
BOSS: Benchmark for Observation Space Shift in Long-Horizon Task—0
A New Path: Scaling Vision-and-Language Navigation with Synthetic Instructions and Imitation Learning—0
DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching—0
Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight—0
A New Framework for Query Efficient Active Imitation Learning—0
A Comparison of Policy Search in Joint Space and Cartesian Space for Refinement of Skills—0
Bootstrapping Imitation Learning for Long-horizon Manipulation via Hierarchical Data Collection Space—0
A New Corpus and Imitation Learning Framework for Context-Dependent Semantic Parsing—0
3D Ego-Pose Estimation via Imitation Learning—0
BOOK: Storing Algorithm-Invariant Episodes for Deep Reinforcement Learning—0
BlockPuzzle - A Challenge in Physical Reasoning and Generalization for Robot Learning—0
An Energy-Aware Online Learning Framework for Resource Management in Heterogeneous Platforms—0
Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning—0
Blending Imitation and Reinforcement Learning for Robust Policy Improvement—0
Adaptive Visual Imitation Learning for Robotic Assisted Feeding Across Varied Bowl Configurations and Food Types—0
Adaptive t-Momentum-based Optimization for Unknown Ratio of Outliers in Amateur Data in Imitation Learning—0
Bi-LAT: Bilateral Control-Based Imitation Learning via Natural Language and Action Chunking with Transformers—0
ACE: A Cross-Platform Visual-Exoskeletons System for Low-Cost Dexterous Teleoperation—0
An Analysis of Logit Learning with the r-Lambert Function—0
A Comparison of Imitation Learning Algorithms for Bimanual Manipulation—0
Differentiable Constrained Imitation Learning for Robot Motion Planning and Control—0
Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach—0
Adaptive Neural Networks Using Residual Fitting—0
An Algorithmic Perspective on Imitation Learning—0
Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration—0
Adaptive Synthetic Characters for Military Training—0
BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay—0
Benchmarking Sample Selection Strategies for Batch Reinforcement Learning—0
Benchmarking Mobile Device Control Agents across Diverse Configurations—0
Bellman Diffusion Models—0
An Adaptive Human Driver Model for Realistic Race Car Simulations—0
DIDA: Denoised Imitation Learning based on Domain Adaptation—0
Differentiable Robust LQR Layers—0
Diffusion Imitation from Observation—0
Improving Behavioural Cloning with Positive Unlabeled Learning—0
Behavioural Cloning in VizDoom—0
Adaptive Adversarial Imitation Learning—0
Behavior-Targeted Attack on Reinforcement Learning with Limited Access to Victim's Policy—0
Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets—0
Towards Biosignals-Free Autonomous Prosthetic Hand Control via Imitation Learning—0
Amortized nonmyopic active search via deep imitation learning—0
Behavioral Cloning via Search in Video PreTraining Latent Space—0
Error-based or target-based? A unifying framework for learning in recurrent spiking networks—0
Amortized Noisy Channel Neural Machine Translation—0
Adapting by Analogy: OOD Generalization of Visuomotor Policies via Functional Correspondence—0
VITAL: Interactive Few-Shot Imitation Learning via Visual Human-in-the-Loop Corrections—0
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