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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 401–450 of 2122 papers

TitleStatusHype
Delayed Reinforcement Learning by Imitation—0
Deep Reinforcement Learning with Mixed Convolutional Network—0
Amortized nonmyopic active search via deep imitation learning—0
Behavioral Cloning via Search in Video PreTraining Latent Space—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
Domain Adaptive Imitation Learning with Visual Observation—0
A Model-Based Approach to Imitation Learning through Multi-Step Predictions—0
Behavioral Cloning from Noisy Demonstrations—0
DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning—0
AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control—0
BEAST: Efficient Tokenization of B-Splines Encoded Action Sequences for Imitation Learning—0
Adapting a World Model for Trajectory Following in a 3D Game—0
Divide and Repair: Using Options to Improve Performance of Imitation Learning Against Adversarial Demonstrations—0
BEAC: Imitating Complex Exploration and Task-oriented Behaviors for Invisible Object Nonprehensile Manipulation—0
Deep imitation learning for molecular inverse problems—0
Diverse Imitation Learning via Self-Organizing Generative Models—0
BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning—0
Task-Agnostic Learning to Accomplish New Tasks—0
Offline Diversity Maximization Under Imitation Constraints—0
Accelerating Training in Pommerman with Imitation and Reinforcement Learning—0
Adapt3R: Adaptive 3D Scene Representation for Domain Transfer in Imitation Learning—0
Leveraging Human Guidance for Deep Reinforcement Learning Tasks—0
Deep Learning for Visual Navigation of Underwater Robots—0
Deep Learning of Robotic Tasks without a Simulator using Strong and Weak Human Supervision—0
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction—0
Deep-MPC: A DAGGER-Driven Imitation Learning Strategy for Optimal Constrained Battery Charging—0
Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations—0
Deep Reinforcement Learning-based Multi-objective Path Planning on the Off-road Terrain Environment for Ground Vehicles—0
Deep Reinforcement Learning for Autonomous Driving: A Survey—0
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch—0
Deep Reinforcement Learning for Personalized Search Story Recommendation—0
Domain-adapted Learning and Imitation: DRL for Power Arbitrage—0
Diverse Imitation Learning via Self-OrganizingGenerative Models—0
Diverse Policies Recovering via Pointwise Mutual Information Weighted Imitation Learning—0
Document Level Hierarchical Transformer—0
Domain-Adversarial and Conditional State Space Model for Imitation Learning—0
Deep Bayesian Reward Learning from Preferences—0
Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning—0
Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC—0
Bayesian Multi-type Mean Field Multi-agent Imitation Learning—0
Behavioural Cloning in VizDoom—0
Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding—0
A Linearly Constrained Nonparametric Framework for Imitation Learning—0
Design and Control of Roller Grasper V2 for In-Hand Manipulation—0
Decoupling Skill Learning from Robotic Control for Generalizable Object Manipulation—0
Bayesian Learning for Dynamic Inference—0
Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces—0
Distributional Decision Transformer for Hindsight Information Matching—0
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