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 741–750 of 2122 papers

TitleStatusHype
Infer and Adapt: Bipedal Locomotion Reward Learning from Demonstrations via Inverse Reinforcement Learning—0
CasIL: Cognizing and Imitating Skills via a Dual Cognition-Action Architecture—0
Symbolic Imitation Learning: From Black-Box to Explainable Driving Policies—0
Hierarchical Imitation Learning for Stochastic Environments—0
Offline to Online Learning for Real-Time Bandwidth Estimation—0
Improving Generalization in Game Agents with Data Augmentation in Imitation Learning—0
Learning to Drive Anywhere—0
Cloud-Based Hierarchical Imitation Learning for Scalable Transfer of Construction Skills from Human Workers to Assisting Robots—0
CASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters—0
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions—0
Show:102550
← PrevPage 75 of 213Next →

No leaderboard results yet.