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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 1751–1800 of 2122 papers

TitleStatusHype
Learning to request guidance in emergent language—0
Seeded self-play for language learning—0
Plan Arithmetic: Compositional Plan Vectors for Multi-Task Control—0
Learning a Safety Verifiable Adaptive Cruise Controller from Human Driving Data—0
Learning Latent Process from High-Dimensional Event Sequences via Efficient SamplingCode0
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement LearningCode0
Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement LearningCode0
Optimal Power Flow Using Graph Neural NetworksCode1
Conditional Driving from Natural Language Instructions—0
Learning chordal extensions—0
Topological Navigation Graph Framework—0
Imitating by generating: deep generative models for imitation of interactive tasks—0
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement—0
Model-based Behavioral Cloning with Future Image Similarity LearningCode0
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?—0
Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping—0
Review of Learning-based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps between Self-driving and Traffic Congestion—0
Learning Calibratable Policies using Programmatic Style-ConsistencyCode0
Task-Relevant Adversarial Imitation Learning—0
INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps—0
Tensor-based Cooperative Control for Large Scale Multi-intersection Traffic Signal Using Deep Reinforcement Learning and Imitation Learning—0
Domain Adaptive Imitation LearningCode0
Zero-shot Imitation Learning from Demonstrations for Legged Robot Visual Navigation—0
RLBench: The Robot Learning Benchmark & Learning EnvironmentCode0
Imitation Learning of Robot Policies using Language, Vision and Motion—0
Cross Domain Imitation Learning—0
Learning Effective Exploration Strategies For Contextual Bandits—0
Adaptive Adversarial Imitation Learning—0
Policy Optimization by Local Improvement through Search—0
Partial Simulation for Imitation Learning—0
Towards Scalable Imitation Learning for Multi-Agent Systems with Graph Neural Networks—0
MoET: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees—0
Learning to Reach Goals Without Reinforcement Learning—0
Self-Imitation Learning via Trajectory-Conditioned Policy for Hard-Exploration Tasks—0
Support-guided Adversarial Imitation Learning—0
Goal-Conditioned Video Prediction—0
ASYNCHRONOUS MULTI-AGENT GENERATIVE ADVERSARIAL IMITATION LEARNING—0
Accept Synthetic Objects as Real: End-to-End Training of Attentive Deep Visuomotor Policies for Manipulation in ClutterCode0
Avoidance Learning Using Observational Reinforcement Learning—0
Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic SupervisorCode0
Leveraging Human Guidance for Deep Reinforcement Learning Tasks—0
Safer End-to-End Autonomous Driving via Conditional Imitation Learning and Command Augmentation—0
Learning Your Way Without Map or Compass: Panoramic Target Driven Visual Navigation—0
Self-Supervised Correspondence in Visuomotor Policy LearningCode0
Learning Visuomotor Policies for Aerial Navigation Using Cross-Modal RepresentationsCode0
State Representation Learning from Demonstration—0
A Linearly Constrained Nonparametric Framework for Imitation Learning—0
VILD: Variational Imitation Learning with Diverse-quality Demonstrations—0
Deep attention networks reveal the rules of collective motion in zebrafishCode0
MPC-Net: A First Principles Guided Policy SearchCode0
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