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 1326–1350 of 2122 papers

TitleStatusHype
To Follow or not to Follow: Selective Imitation Learning from Observations—0
Tool-as-Interface: Learning Robot Policies from Human Tool Usage through Imitation Learning—0
Topological Navigation Graph Framework—0
Touch begins where vision ends: Generalizable policies for contact-rich manipulation—0
Toward Imitating Visual Attention of Experts in Software Development Tasks—0
Towards an Adaptable and Generalizable Optimization Engine in Decision and Control: A Meta Reinforcement Learning Approach—0
Towards a Reward-Free Reinforcement Learning Framework for Vehicle Control—0
Towards Data-Driven Automatic Video Editing—0
Towards Embodied Scene Description—0
Towards Fusing Point Cloud and Visual Representations for Imitation Learning—0
Towards Human-Like Driving: Active Inference in Autonomous Vehicle Control—0
Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds—0
Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning—0
Towards More Generalizable One-shot Visual Imitation Learning—0
Towards Scalable Imitation Learning for Multi-Agent Systems with Graph Neural Networks—0
Toward the Fundamental Limits of Imitation Learning—0
Tracking the Race Between Deep Reinforcement Learning and Imitation Learning -- Extended Version—0
TRAIL: Near-Optimal Imitation Learning with Suboptimal Data—0
Training Electric Vehicle Charging Controllers with Imitation Learning—0
Training Neural Network Controllers Using Control Barrier Functions in the Presence of Disturbances—0
Training Robots without Robots: Deep Imitation Learning for Master-to-Robot Policy Transfer—0
Trajectory VAE for multi-modal imitation—0
Transferable Reward Learning by Dynamics-Agnostic Discriminator Ensemble—0
Transfering Hierarchical Structure with Dual Meta Imitation Learning—0
Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges—0
Show:102550
← PrevPage 54 of 85Next →

No leaderboard results yet.