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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 51–75 of 2122 papers

TitleStatusHype
Inverse Q-Learning Done Right: Offline Imitation Learning in Q^π-Realizable MDPsCode0
ReasonPlan: Unified Scene Prediction and Decision Reasoning for Closed-loop Autonomous DrivingCode1
Online Knowledge Distillation with Reward Guidance—0
Structured Reinforcement Learning for Combinatorial Decision-MakingCode1
MisoDICE: Multi-Agent Imitation from Unlabeled Mixed-Quality Demonstrations—0
ProgRM: Build Better GUI Agents with Progress Rewards—0
Dynamic Manipulation of Deformable Objects in 3D: Simulation, Benchmark and Learning Strategy—0
Bootstrapping Imitation Learning for Long-horizon Manipulation via Hierarchical Data Collection Space—0
Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only—0
Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)—0
Reinforcement Twinning for Hybrid Control of Flapping-Wing Drones—0
UAV-Flow Colosseo: A Real-World Benchmark for Flying-on-a-Word UAV Imitation Learning—0
FLARE: Robot Learning with Implicit World Modeling—0
Guided Policy Optimization under Partial ObservabilityCode0
Learning-based Autonomous Oversteer Control and Collision Avoidance—0
Imitation Learning via Focused Satisficing—0
Structured Agent Distillation for Large Language Model—0
KinTwin: Imitation Learning with Torque and Muscle Driven Biomechanical Models Enables Precise Replication of Able-Bodied and Impaired Movement from Markerless Motion Capture—0
Zero-Shot Visual Generalization in Robot Manipulation—0
EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video—0
IN-RIL: Interleaved Reinforcement and Imitation Learning for Policy Fine-TuningCode0
EnerVerse-AC: Envisioning Embodied Environments with Action Condition—0
FoldNet: Learning Generalizable Closed-Loop Policy for Garment Folding via Keypoint-Driven Asset and Demonstration Synthesis—0
Imitation Learning for Adaptive Control of a Virtual Soft Exoglove—0
Distilling Realizable Students from Unrealizable Teachers—0
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