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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 776–800 of 2122 papers

TitleStatusHype
Deep Reinforcement Learning with Mixed Convolutional Network—0
Amortized nonmyopic active search via deep imitation learning—0
Domain-adapted Learning and Imitation: DRL for Power Arbitrage—0
Deep Reinforcement Learning for Personalized Search Story Recommendation—0
Behavioral Cloning via Search in Video PreTraining Latent Space—0
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch—0
Deep Reinforcement Learning for Autonomous Driving: A Survey—0
Error-based or target-based? A unifying framework for learning in recurrent spiking networks—0
Amortized Noisy Channel Neural Machine Translation—0
Adapting by Analogy: OOD Generalization of Visuomotor Policies via Functional Correspondence—0
Deep Reinforcement Learning-based Multi-objective Path Planning on the Off-road Terrain Environment for Ground Vehicles—0
Deep-MPC: A DAGGER-Driven Imitation Learning Strategy for Optimal Constrained Battery Charging—0
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction—0
Behavioral Cloning from Noisy Demonstrations—0
A Model-Based Approach to Imitation Learning through Multi-Step Predictions—0
Deep Learning of Robotic Tasks without a Simulator using Strong and Weak Human Supervision—0
Deep Learning for Visual Navigation of Underwater Robots—0
BEAST: Efficient Tokenization of B-Splines Encoded Action Sequences for Imitation Learning—0
AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control—0
Adapting a World Model for Trajectory Following in a 3D Game—0
Deep imitation learning for molecular inverse problems—0
BEAC: Imitating Complex Exploration and Task-oriented Behaviors for Invisible Object Nonprehensile Manipulation—0
BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning—0
Task-Agnostic Learning to Accomplish New Tasks—0
Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations—0
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