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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 2001–2025 of 2122 papers

TitleStatusHype
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image TranslationCode0
Following High-level Navigation Instructions on a Simulated Quadcopter with Imitation LearningCode1
Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learning—0
Fast Policy Learning through Imitation and Reinforcement—0
Learning Self-Imitating Diverse Policies—0
Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement LearningCode0
Inverse Rational Control: Inferring What You Think from How You Forage—0
Verifiable Reinforcement Learning via Policy ExtractionCode1
Multi-task Maximum Entropy Inverse Reinforcement LearningCode0
Maximum Causal Tsallis Entropy Imitation Learning—0
Imitating Latent Policies from ObservationCode1
End-to-end driving simulation via angle branched network—0
Learning Temporal Strategic Relationships using Generative Adversarial Imitation Learning—0
Behavioral Cloning from ObservationCode1
Zero-Shot Visual ImitationCode0
Event Extraction with Generative Adversarial Imitation Learning—0
Socially Guided Intrinsic Motivation for Robot Learning of Motor Skills—0
Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue SystemsCode0
Learning Contracting Vector Fields For Stable Imitation Learning—0
Hindsight is Only 50/50: Unsuitability of MDP based Approximate POMDP Solvers for Multi-resolution Information Gathering—0
Learning to Search via Retrospective Imitation—0
Universal Planning NetworksCode0
Visual Robot Task PlanningCode0
Safe end-to-end imitation learning for model predictive control—0
Generating Multi-Agent Trajectories using Programmatic Weak SupervisionCode0
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