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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 1601–1625 of 2122 papers

TitleStatusHype
BlockPuzzle - A Challenge in Physical Reasoning and Generalization for Robot Learning—0
BOOK: Storing Algorithm-Invariant Episodes for Deep Reinforcement Learning—0
Bootstrapping Imitation Learning for Long-horizon Manipulation via Hierarchical Data Collection Space—0
Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight—0
BOSS: Benchmark for Observation Space Shift in Long-Horizon Task—0
Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulation—0
Bridging Imitation and Online Reinforcement Learning: An Optimistic Tale—0
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism—0
Bridging the Communication Gap: Artificial Agents Learning Sign Language through Imitation—0
Bridging the Imitation Gap by Adaptive Insubordination—0
Bridging the Resource Gap: Deploying Advanced Imitation Learning Models onto Affordable Embedded Platforms—0
Bridging the Sim-to-real Gap: A Control Framework for Imitation Learning of Model Predictive Control—0
Building an Automated Gesture Imitation Game for Teenagers with ASD—0
Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learning—0
Burn-In Demonstrations for Multi-Modal Imitation Learning—0
CAG-QIL: Context-Aware Actionness Grouping via Q Imitation Learning for Online Temporal Action Localization—0
CALM: Conditional Adversarial Latent Models for Directable Virtual Characters—0
CAMBranch: Contrastive Learning with Augmented MILPs for Branching—0
CANDERE-COACH: Reinforcement Learning from Noisy Feedback—0
CANVAS: Commonsense-Aware Navigation System for Intuitive Human-Robot Interaction—0
CAPS: Context-Aware Priority Sampling for Enhanced Imitation Learning in Autonomous Driving—0
CasIL: Cognizing and Imitating Skills via a Dual Cognition-Action Architecture—0
Causal Imitability Under Context-Specific Independence Relations—0
Causal Imitation Learning with Unobserved Confounders—0
Causal Robot Communication Inspired by Observational Learning Insights—0
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