SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1661–1670 of 2122 papers

TitleStatusHype
Active Imitation Learning from Multiple Non-Deterministic Teachers: Formulation, Challenges, and Algorithms—0
Self-Imitation Learning via Generalized Lower Bound Q-learning—0
PAC Bounds for Imitation and Model-based Batch Learning of Contextual Markov Decision Processes—0
Modeling Human Driving Behavior through Generative Adversarial Imitation Learning—0
Stealing Deep Reinforcement Learning Models for Fun and Profit—0
Primal Wasserstein Imitation LearningCode0
Explaining Autonomous Driving by Learning End-to-End Visual Attention—0
Cross-Domain Imitation Learning with a Dual Structure—0
NewtonianVAE: Proportional Control and Goal Identification from Pixels via Physical Latent Spaces—0
Imitative Non-Autoregressive Modeling for Trajectory Forecasting and Imputation—0
Show:102550
← PrevPage 167 of 213Next →

No leaderboard results yet.