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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 701–750 of 2122 papers

TitleStatusHype
Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning—0
MimicGen: A Data Generation System for Scalable Robot Learning using Human DemonstrationsCode2
Model-Based Runtime Monitoring with Interactive Imitation Learning—0
MimicTouch: Leveraging Multi-modal Human Tactile Demonstrations for Contact-rich Manipulation—0
What Makes it Ok to Set a Fire? Iterative Self-distillation of Contexts and Rationales for Disambiguating Defeasible Social and Moral Situations—0
Data-driven Traffic Simulation: A Comprehensive Review—0
Good Better Best: Self-Motivated Imitation Learning for noisy Demonstrations—0
WebWISE: Web Interface Control and Sequential Exploration with Large Language Models—0
Human-in-the-Loop Task and Motion Planning for Imitation Learning—0
Promoting Generalization for Exact Solvers via Adversarial Instance Augmentation—0
Robust Visual Imitation Learning with Inverse Dynamics Representations—0
Learning Generalizable Manipulation Policies with Object-Centric 3D Representations—0
Learning to Discern: Imitating Heterogeneous Human Demonstrations with Preference and Representation Learning—0
LeTFuser: Light-weight End-to-end Transformer-Based Sensor Fusion for Autonomous Driving with Multi-Task LearningCode1
Few-Shot In-Context Imitation Learning via Implicit Graph Alignment—0
One-Shot Imitation Learning: A Pose Estimation Perspective—0
Efficient Online Learning with Offline Datasets for Infinite Horizon MDPs: A Bayesian Approach—0
Mimicking the Maestro: Exploring the Efficacy of a Virtual AI Teacher in Fine Motor Skill Acquisition—0
Progressively Efficient Learning—0
Towards Example-Based NMT with Multi-Levenshtein TransformersCode0
Generative Intrinsic Optimization: Intrinsic Control with Model Learning—0
Cross-Episodic Curriculum for Transformer Agents—0
RoboCLIP: One Demonstration is Enough to Learn Robot PoliciesCode1
Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning—0
Imitation Learning from Purified DemonstrationsCode0
Imitation Learning from Observation with Automatic Discount SchedulingCode1
Inverse Factorized Q-Learning for Cooperative Multi-agent Imitation Learning—0
Zero-Shot Transfer in Imitation Learning—0
Reinforcement Learning in the Era of LLMs: What is Essential? What is needed? An RL Perspective on RLHF, Prompting, and BeyondCode1
Memory-Consistent Neural Networks for Imitation Learning—0
TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models—0
Imitator Learning: Achieve Out-of-the-Box Imitation Ability in Variable Environments—0
Offline Imitation Learning with Variational Counterfactual ReasoningCode0
Blending Imitation and Reinforcement Learning for Robust Policy Improvement—0
STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descent—0
Imitation Learning from Observation through Optimal Transport—0
Interpretable Imitation Learning with Dynamic Causal Relations—0
Learning Decentralized Flocking Controllers with Spatio-Temporal Graph Neural Network—0
ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem SolvingCode3
Adversarial Imitation Learning from Visual Observations using Latent InformationCode0
Infer and Adapt: Bipedal Locomotion Reward Learning from Demonstrations via Inverse Reinforcement Learning—0
CasIL: Cognizing and Imitating Skills via a Dual Cognition-Action Architecture—0
Symbolic Imitation Learning: From Black-Box to Explainable Driving Policies—0
Hierarchical Imitation Learning for Stochastic Environments—0
Offline to Online Learning for Real-Time Bandwidth Estimation—0
Improving Generalization in Game Agents with Data Augmentation in Imitation Learning—0
Learning to Drive Anywhere—0
Cloud-Based Hierarchical Imitation Learning for Scalable Transfer of Construction Skills from Human Workers to Assisting Robots—0
CASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters—0
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions—0
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