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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 1776–1800 of 2122 papers

TitleStatusHype
DITTO: Offline Imitation Learning with World Models—0
Diverse Imitation Learning via Self-OrganizingGenerative Models—0
Diverse Imitation Learning via Self-Organizing Generative Models—0
Offline Diversity Maximization Under Imitation Constraints—0
Diverse Policies Recovering via Pointwise Mutual Information Weighted Imitation Learning—0
Divide and Repair: Using Options to Improve Performance of Imitation Learning Against Adversarial Demonstrations—0
DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning—0
Document Level Hierarchical Transformer—0
Domain Adaptive Imitation Learning with Visual Observation—0
Domain-Adversarial and Conditional State Space Model for Imitation Learning—0
DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks—0
Dream to Manipulate: Compositional World Models Empowering Robot Imitation Learning with Imagination—0
DriveGPT4-V2: Harnessing Large Language Model Capabilities for Enhanced Closed-Loop Autonomous Driving—0
Driving in Real Life with Inverse Reinforcement Learning—0
DropoutDAgger: A Bayesian Approach to Safe Imitation Learning—0
D-Shape: Demonstration-Shaped Reinforcement Learning via Goal Conditioning—0
Dyna-AIL : Adversarial Imitation Learning by Planning—0
Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination—0
Interpretable Imitation Learning with Dynamic Causal Relations—0
Dynamic Feature Selection for Dependency Parsing—0
Dynamic Inference—0
Dynamic Manipulation of Deformable Objects in 3D: Simulation, Benchmark and Learning Strategy—0
Dynamic Modeling of Hand-Object Interactions via Tactile Sensing—0
Dynamic Modeling of Hand-Object Interactions via Tactile Sensing.—0
DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control—0
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