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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 2051–2100 of 2122 papers

TitleStatusHype
Predictive-State Decoders: Encoding the Future into Recurrent Networks—0
Avoidance of Manual Labeling in Robotic Autonomous Navigation Through Multi-Sensory Semi-Supervised Learning—0
OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement LearningCode0
DropoutDAgger: A Bayesian Approach to Safe Imitation Learning—0
One-Shot Visual Imitation Learning via Meta-LearningCode0
Imitation Learning for Vision-based Lane Keeping Assistance—0
BOOK: Storing Algorithm-Invariant Episodes for Deep Reinforcement Learning—0
Learning What's Easy: Fully Differentiable Neural Easy-First Taggers—0
Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation—0
STARDATA: A StarCraft AI Research DatasetCode0
Local Bayesian Optimization of Motor Skills—0
End-to-End Differentiable Adversarial Imitation Learning—0
RAIL: Risk-Averse Imitation LearningCode0
Merge or Not? Learning to Group Faces via Imitation LearningCode0
Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context TranslationCode0
Robust Imitation of Diverse Behaviors—0
A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning—0
Learning human behaviors from motion capture by adversarial imitationCode0
Path Integral Networks: End-to-End Differentiable Optimal Control—0
Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning—0
Gated-Attention Architectures for Task-Oriented Language GroundingCode0
Meta learning Framework for Automated Driving—0
Visuospatial Skill Learning for Robots—0
The Atari Grand Challenge DatasetCode0
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets—0
Visual Semantic Planning using Deep Successor Representations—0
Repeated Inverse Reinforcement Learning—0
Optimizing Differentiable Relaxations of Coreference Evaluation MetricsCode0
Deep Q-learning from DemonstrationsCode0
Imitation learning for structured prediction in natural language processing—0
DART: Noise Injection for Robust Imitation LearningCode1
InfoGAIL: Interpretable Imitation Learning from Visual DemonstrationsCode0
One-Shot Imitation Learning—0
Coordinated Multi-Agent Imitation Learning—0
Third-Person Imitation LearningCode0
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction—0
How hard is it to cross the room? -- Training (Recurrent) Neural Networks to steer a UAV—0
The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI—0
Imitating Driver Behavior with Generative Adversarial NetworksCode0
A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to ImitationCode0
Unsupervised Perceptual Rewards for Imitation Learning—0
Model-based Adversarial Imitation Learning—0
Deep Learning of Robotic Tasks without a Simulator using Strong and Weak Human Supervision—0
Imitation learning for language generation from unaligned data—0
Learning to Gather Information via Imitation—0
A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based ModelsCode0
A Study of Imitation Learning Methods for Semantic Role Labeling—0
SHEF-MIME: Word-level Quality Estimation Using Imitation Learning—0
Noise reduction and targeted exploration in imitation learning for Abstract Meaning Representation parsing—0
Imitation Learning with Recurrent Neural Networks—0
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