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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 1651–1700 of 2122 papers

TitleStatusHype
Flexible and Efficient Long-Range Planning Through Curious Exploration—0
Energy-Based Imitation LearningCode1
Design and Control of Roller Grasper V2 for In-Hand Manipulation—0
Approximate Inverse Reinforcement Learning from Vision-based Imitation Learning—0
Zero-Shot Compositional Policy Learning via Language GroundingCode1
Imitation Learning for Fashion Style Based on Hierarchical Multimodal Representation—0
Knowledge Distillation for Mobile Edge Computation Offloading—0
TextGAIL: Generative Adversarial Imitation Learning for Text Generation—0
State-Only Imitation Learning for Dexterous Manipulation—0
Learning Agile Robotic Locomotion Skills by Imitating Animals—0
Learning Sparse Rewarded Tasks from Sub-Optimal DemonstrationsCode0
Augmented Q Imitation Learning (AQIL)Code0
HATSUKI : An anime character like robot figure platform with anime-style expressions and imitation learning based action generation—0
Modeling 3D Shapes by Reinforcement LearningCode1
Counterfactual Policy Evaluation for Decision-Making in Autonomous DrivingCode1
An Energy-Aware Online Learning Framework for Resource Management in Heterogeneous Platforms—0
Goal-Conditioned End-to-End Visuomotor Control for Versatile Skill PrimitivesCode0
Sparse Graphical Memory for Robust PlanningCode1
Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations—0
MQA: Answering the Question via Robotic ManipulationCode0
Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning—0
PLOP: Probabilistic poLynomial Objects trajectory Planning for autonomous driving—0
Human AI interaction loop training: New approach for interactive reinforcement learning—0
Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate—0
A Geometric Perspective on Visual Imitation Learning—0
MPC-guided Imitation Learning of Neural Network Policies for the Artificial PancreasCode0
Causal Transfer for Imitation Learning and Decision Making under Sensor-shift—0
How Do We Move: Modeling Human Movement with System Dynamics—0
Efficiently Guiding Imitation Learning Agents with Human Gaze—0
State-only Imitation with Transition Dynamics MismatchCode1
Provably Efficient Third-Person Imitation from Offline Observation—0
Metric-Based Imitation Learning Between Two Dissimilar Anthropomorphic Robotic Arms—0
Scalable Multi-Task Imitation Learning with Autonomous Improvement—0
Provable Representation Learning for Imitation Learning via Bi-level Optimization—0
Estimating Q(s,s') with Deep Deterministic Dynamics GradientsCode1
Safe Imitation Learning via Fast Bayesian Reward Inference from PreferencesCode1
Support-weighted Adversarial Imitation Learning—0
Follow the Neurally-Perturbed Leader for Adversarial TrainingCode0
Universal Value Density Estimation for Imitation Learning and Goal-Conditioned Reinforcement LearningCode0
Parameterizing Branch-and-Bound Search Trees to Learn Branching PoliciesCode1
Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning—0
GLSearch: Maximum Common Subgraph Detection via Learning to Search—0
Deep Reinforcement Learning for Autonomous Driving: A Survey—0
Combating False Negatives in Adversarial Imitation Learning—0
Preventing Imitation Learning with Adversarial Policy Ensembles—0
Domain-Adversarial and Conditional State Space Model for Imitation Learning—0
Exploration Based Language Learning for Text-Based Games—0
A Probabilistic Framework for Imitating Human Race Driver Behavior—0
Augmenting GAIL with BC for sample efficient imitation learningCode1
Nested-Wasserstein Self-Imitation Learning for Sequence Generation—0
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