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 431–440 of 2122 papers

TitleStatusHype
Deep Reinforcement Learning for Autonomous Driving: A Survey—0
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch—0
Deep Reinforcement Learning for Personalized Search Story Recommendation—0
Domain-adapted Learning and Imitation: DRL for Power Arbitrage—0
Diverse Imitation Learning via Self-OrganizingGenerative Models—0
Deep Bayesian Reward Learning from Preferences—0
Bayesian Multi-type Mean Field Multi-agent Imitation Learning—0
A Linearly Constrained Nonparametric Framework for Imitation Learning—0
Decoupling Skill Learning from Robotic Control for Generalizable Object Manipulation—0
Bayesian Learning for Dynamic Inference—0
Show:102550
← PrevPage 44 of 213Next →

No leaderboard results yet.