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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

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Showing 1151–1175 of 2122 papers

TitleStatusHype
Advanced Skills through Multiple Adversarial Motion Priors in Reinforcement Learning—0
Self-Imitation Learning from Demonstrations—0
Teachable Reinforcement Learning via Advice DistillationCode1
Goal-conditioned dual-action imitation learning for dexterous dual-arm robot manipulation—0
An Imitation Learning Curriculum for Text Editing with Non-Autoregressive Models—0
MotionAug: Augmentation with Physical Correction for Human Motion PredictionCode1
Causal Robot Communication Inspired by Observational Learning Insights—0
Combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging taskCode0
Policy Architectures for Compositional Generalization in Control—0
Learning Sensorimotor Primitives of Sequential Manipulation Tasks from Visual Demonstrations—0
Learning Solution Manifolds for Control Problems via Energy Minimization—0
Find a Way Forward: a Language-Guided Semantic Map Navigator—0
MIRROR: Differentiable Deep Social Projection for Assistive Human-Robot CommunicationCode0
Bridging the Gap Between Learning in Discrete and Continuous Environments for Vision-and-Language NavigationCode1
Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy OptimizationCode0
Learning Category-Level Generalizable Object Manipulation Policy via Generative Adversarial Self-Imitation Learning from DemonstrationsCode1
An Adaptive Human Driver Model for Realistic Race Car Simulations—0
Fail-Safe Adversarial Generative Imitation LearningCode0
A Versatile Agent for Fast Learning from Human Instructors—0
LISA: Learning Interpretable Skill Abstractions from LanguageCode0
LobsDICE: Offline Learning from Observation via Stationary Distribution Correction EstimationCode1
All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RLCode1
Transporters with Visual Foresight for Solving Unseen Rearrangement Tasks—0
Multi-Task Conditional Imitation Learning for Autonomous Navigation at Crowded Intersections—0
CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories—0
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