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 551–575 of 2122 papers

TitleStatusHype
Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation—0
Cross-Domain Imitation Learning with a Dual Structure—0
Autonomous Navigation in Complex Environments—0
Cross Domain Imitation Learning—0
Automating Deformable Gasket Assembly—0
A Geometric Perspective on Visual Imitation Learning—0
AdaCred: Adaptive Causal Decision Transformers with Feature Crediting—0
ExACT: An End-to-End Autonomous Excavator System Using Action Chunking With Transformers—0
Cross-domain Imitation from Observations—0
ACT-JEPA: Joint-Embedding Predictive Architecture Improves Policy Representation Learning—0
CRISP: Curriculum Inducing Primitive Informed Subgoal Prediction for Hierarchical Reinforcement Learning—0
AgentMixer: Multi-Agent Correlated Policy Factorization—0
Accelerating Interactive Human-like Manipulation Learning with GPU-based Simulation and High-quality Demonstrations—0
Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning—0
Coupled Distributional Random Expert Distillation for World Model Online Imitation Learning—0
Automatic Curricula via Expert Demonstrations—0
Automated Task-Time Interventions to Improve Teamwork using Imitation Learning—0
Evolutionary Selective Imitation: Interpretable Agents by Imitation Learning Without a Demonstrator—0
Evolution of cooperation in the public goods game with Q-learning—0
Cortical Mirror-System Activation During Real-Life Game Playing: An Intracranial Electroencephalography (EEG) Study—0
Correlation Priors for Reinforcement Learning—0
Automated Feature Selection for Inverse Reinforcement Learning—0
Auto-Encoding Inverse Reinforcement Learning—0
CordViP: Correspondence-based Visuomotor Policy for Dexterous Manipulation in Real-World—0
Active Third-Person Imitation Learning—0
Show:102550
← PrevPage 23 of 85Next →

No leaderboard results yet.