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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 1726–1750 of 2122 papers

TitleStatusHype
Deep-MPC: A DAGGER-Driven Imitation Learning Strategy for Optimal Constrained Battery Charging—0
Deep Reinforcement Learning-based Multi-objective Path Planning on the Off-road Terrain Environment for Ground Vehicles—0
Deep Reinforcement Learning for Autonomous Driving: A Survey—0
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch—0
Deep Reinforcement Learning for Personalized Search Story Recommendation—0
Domain-adapted Learning and Imitation: DRL for Power Arbitrage—0
Deep Reinforcement Learning with Mixed Convolutional Network—0
Delayed Reinforcement Learning by Imitation—0
DemoDICE: Offline Imitation Learning with Supplementary Imperfect Demonstrations—0
Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning—0
Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC—0
Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding—0
Design and Control of Roller Grasper V2 for In-Hand Manipulation—0
Deterministic Policy Imitation Gradient Algorithm—0
Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces—0
Bootstrapping Developmental AIs: From Simple Competences to Intelligent Human-Compatible AIs—0
DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation—0
DexGraspVLA: A Vision-Language-Action Framework Towards General Dexterous Grasping—0
DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning—0
DexterityGen: Foundation Controller for Unprecedented Dexterity—0
Dexterous Imitation Made Easy: A Learning-Based Framework for Efficient Dexterous Manipulation—0
Dexterous Manipulation through Imitation Learning: A Survey—0
DIAL: Distribution-Informed Adaptive Learning of Multi-Task Constraints for Safety-Critical Systems—0
DIDA: Denoised Imitation Learning based on Domain Adaptation—0
Differentiable Constrained Imitation Learning for Robot Motion Planning and Control—0
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