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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 1926–1950 of 2122 papers

TitleStatusHype
FUNCTO: Function-Centric One-Shot Imitation Learning for Tool Manipulation—0
FürElise: Capturing and Physically Synthesizing Hand Motions of Piano Performance—0
GAN-Based Interactive Reinforcement Learning from Demonstration and Human Evaluative Feedback—0
GAN-MPC: Training Model Predictive Controllers with Parameterized Cost Functions using Demonstrations from Non-identical Experts—0
Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks—0
Gaze-based dual resolution deep imitation learning for high-precision dexterous robot manipulation—0
Gaze Training by Modulated Dropout Improves Imitation Learning—0
Generalizability of Graph Neural Networks for Decentralized Unlabeled Motion Planning—0
Generalizable Human-Robot Collaborative Assembly Using Imitation Learning and Force Control—0
Generalizable Imitation Learning from Observation via Inferring Goal Proximity—0
Generalizable Imitation Learning Through Pre-Trained Representations—0
Generalization Capability for Imitation Learning—0
Generalization Through Hand-Eye Coordination: An Action Space for Learning Spatially-Invariant Visuomotor Control—0
Generalized Robot Learning Framework—0
Generalizing to New Tasks via One-Shot Compositional Subgoals—0
Generating Personas for Games with Multimodal Adversarial Imitation Learning—0
Generating Physically Realistic and Directable Human Motions from Multi-Modal Inputs—0
Informed Sampling for Diversity in Concept-to-Text NLG—0
Generating stable molecules using imitation and reinforcement learning—0
Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate—0
Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate—0
Generative Adversarial Imitation Learning for Empathy-based AI—0
Generative Adversarial Imitation Learning for End-to-End Autonomous Driving on Urban Environments—0
Generative Adversarial Self-Imitation Learning—0
Generative Intrinsic Optimization: Intrinsic Control with Model Learning—0
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