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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 1626–1650 of 2122 papers

TitleStatusHype
ADAIL: Adaptive Adversarial Imitation Learning—0
Online Adaptive Learning for Runtime Resource Management of Heterogeneous SoCs—0
Adversarial Imitation Learning via Random Search—0
Forward and inverse reinforcement learning sharing network weights and hyperparameters—0
Visual Imitation Made Easy—0
Imitation Learning for Autonomous Trajectory Learning of Robot Arms in Space—0
Non-Adversarial Imitation Learning and its Connections to Adversarial MethodsCode0
Physics-Based Dexterous Manipulations with Estimated Hand Poses and Residual Reinforcement Learning—0
Tracking the Race Between Deep Reinforcement Learning and Imitation Learning -- Extended Version—0
Concurrent Training Improves the Performance of Behavioral Cloning from Observation—0
Interactive Imitation Learning in State-SpaceCode0
Sample Efficient Interactive End-to-End Deep Learning for Self-Driving Cars with Selective Multi-Class Safe Dataset Aggregation—0
Bayesian Robust Optimization for Imitation LearningCode0
Evaluation metrics for behaviour modeling—0
Bridging the Imitation Gap by Adaptive Insubordination—0
Complex Skill Acquisition Through Simple Skill Imitation Learning—0
Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation—0
A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learning—0
IALE: Imitating Active Learner EnsemblesCode0
Building an Automated Gesture Imitation Game for Teenagers with ASD—0
A Study of Learning Search Approximation in Mixed Integer Branch and Bound: Node Selection in SCIP—0
Decentralized policy learning with partial observation and mechanical constraints for multiperson modelingCode0
Imitation Learning Approach for AI Driving Olympics Trained on Real-world and Simulation Data Simultaneously—0
Explaining Fast Improvement in Online Imitation Learning—0
Policy Improvement via Imitation of Multiple Oracles—0
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