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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 1776–1800 of 2122 papers

TitleStatusHype
Learning chordal extensions—0
Conditional Driving from Natural Language Instructions—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
Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping—0
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?—0
Learning Calibratable Policies using Programmatic Style-ConsistencyCode0
Review of Learning-based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps between Self-driving and Traffic Congestion—0
Task-Relevant Adversarial Imitation Learning—0
INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps—0
Domain Adaptive Imitation LearningCode0
Tensor-based Cooperative Control for Large Scale Multi-intersection Traffic Signal Using Deep Reinforcement Learning and Imitation Learning—0
Zero-shot Imitation Learning from Demonstrations for Legged Robot Visual Navigation—0
RLBench: The Robot Learning Benchmark & Learning EnvironmentCode0
Partial Simulation for Imitation Learning—0
Support-guided Adversarial Imitation Learning—0
Learning to Reach Goals Without Reinforcement Learning—0
MoET: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees—0
Goal-Conditioned Video Prediction—0
Towards Scalable Imitation Learning for Multi-Agent Systems with Graph Neural Networks—0
ASYNCHRONOUS MULTI-AGENT GENERATIVE ADVERSARIAL IMITATION LEARNING—0
Learning Effective Exploration Strategies For Contextual Bandits—0
Cross Domain Imitation Learning—0
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