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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 1826–1850 of 2122 papers

TitleStatusHype
Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning—0
Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning—0
Imitation Learning for Sentence Generation with Dilated Convolutions Using Adversarial TrainingCode0
Learning Vision-based Flight in Drone Swarms by Imitation—0
Comyco: Quality-Aware Adaptive Video Streaming via Imitation LearningCode0
Batch Recurrent Q-Learning for Backchannel Generation Towards Engaging Agents—0
Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation—0
Self-Imitation Learning of Locomotion Movements through Termination CurriculumCode0
Deep Reinforcement Learning for Personalized Search Story Recommendation—0
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards—0
Learning Goal-Oriented Visual Dialog Agents: Imitating and Surpassing Analytic Experts—0
Muscle-actuated Human Simulation and ControlCode0
Towards Data-Driven Automatic Video Editing—0
Leveraging Experience in Lazy Search—0
Improved Reinforcement Learning through Imitation Learning Pretraining Towards Image-based Autonomous Driving—0
Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative SamplingCode0
Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation—0
Imitation-Projected Programmatic Reinforcement Learning—0
Utilizing Eye Gaze to Enhance the Generalization of Imitation Networks to Unseen Environments—0
Better-than-Demonstrator Imitation Learning via Automatically-Ranked DemonstrationsCode0
Hybrid system identification using switching density networksCode0
On-Policy Robot Imitation Learning from a Converging Supervisor—0
Learning a Behavioral Repertoire from Demonstrations—0
Interactive-Predictive Neural Machine Translation through Reinforcement and Imitation—0
Integration of Imitation Learning using GAIL and Reinforcement Learning using Task-achievement Rewards via Probabilistic Graphical Model—0
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