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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 426–450 of 2122 papers

TitleStatusHype
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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