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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 1601–1625 of 2122 papers

TitleStatusHype
Navigation with QPHIL: Quantizing Planner for Hierarchical Implicit Q-Learning—0
PLANRL: A Motion Planning and Imitation Learning Framework to Bootstrap Reinforcement Learning—0
NEARL: Non-Explicit Action Reinforcement Learning for Robotic Control—0
On Generalization of Adversarial Imitation Learning and Beyond—0
NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesis—0
Nested-Wasserstein Self-Imitation Learning for Sequence Generation—0
Neural Column Generation for Capacitated Vehicle Routing—0
Neural Differentiable Integral Control Barrier Functions for Unknown Nonlinear Systems with Input Constraints—0
Neural Dynamic Policies for End-to-End Sensorimotor Learning—0
Neural Multivariate Regression: Qualitative Insights from the Unconstrained Feature Model—0
Neural Random Forest Imitation—0
Neural Rate Control for Video Encoding using Imitation Learning—0
Neuroprosthetic decoder training as imitation learning—0
Neuro-Symbolic Imitation Learning: Discovering Symbolic Abstractions for Skill Learning—0
NewtonianVAE: Proportional Control and Goal Identification from Pixels via Physical Latent Spaces—0
NIFT: Neural Interaction Field and Template for Object Manipulation—0
NIL: No-data Imitation Learning by Leveraging Pre-trained Video Diffusion Models—0
NNSynth: Neural Network Guided Abstraction-Based Controller Synthesis for Stochastic Systems—0
Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation—0
Noise reduction and targeted exploration in imitation learning for Abstract Meaning Representation parsing—0
Object and Contact Point Tracking in Demonstrations Using 3D Gaussian Splatting—0
Object-Centric Action-Enhanced Representations for Robot Visuo-Motor Policy Learning—0
Object-Centric Latent Action Learning—0
ObjectVLA: End-to-End Open-World Object Manipulation Without Demonstration—0
Offline Imitation Learning by Controlling the Effective Planning Horizon—0
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