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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 1401–1450 of 2122 papers

TitleStatusHype
Seeing All the Angles: Learning Multiview Manipulation Policies for Contact-Rich Tasks from DemonstrationsCode1
End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learningCode0
H2O: A Benchmark for Visual Human-human Object Handover Analysis—0
Multi-task Learning with Attention for End-to-end Autonomous Driving—0
Multi-Modal Fusion Transformer for End-to-End Autonomous DrivingCode2
Skeletal Feature Compensation for Imitation Learning with Embodiment Mismatch—0
GAN-Based Interactive Reinforcement Learning from Demonstration and Human Evaluative Feedback—0
Reward function shape exploration in adversarial imitation learning: an empirical study—0
An Adversarial Imitation Click Model for Information RetrievalCode1
Counter-Strike Deathmatch with Large-Scale Behavioural CloningCode1
Data-Driven Simulation of Ride-Hailing Services using Imitation and Reinforcement Learning—0
AMP: Adversarial Motion Priors for Stylized Physics-Based Character ControlCode2
No Need for Interactions: Robust Model-Based Imitation Learning using Neural ODECode0
UAV-Assisted Communication in Remote Disaster Areas using Imitation Learning—0
Learning Online from Corrective Feedback: A Meta-Algorithm for Robotics—0
Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic GraspingCode0
Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images for Autonomous DrivingCode1
DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation—0
iCurb: Imitation Learning-based Detection of Road Curbs using Aerial Images for Autonomous DrivingCode1
LazyDAgger: Reducing Context Switching in Interactive Imitation Learning—0
Learning Lipschitz Feedback Policies from Expert Demonstrations: Closed-Loop Guarantees, Generalization and Robustness—0
ReAgent: Point Cloud Registration using Imitation and Reinforcement LearningCode1
Co-Imitation Learning without Expert Demonstration—0
Imitation Learning from MPC for Quadrupedal Multi-Gait Control—0
Self-Imitation Learning by Planning—0
Adversarial Imitation Learning with Trajectorial Augmentation and CorrectionCode0
On Imitation Learning of Linear Control Policies: Enforcing Stability and Robustness Constraints via LMI Conditions—0
Learning 6DoF Grasping Using Reward-Consistent Demonstration—0
Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks—0
Online Baum-Welch algorithm for Hierarchical Imitation LearningCode0
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism—0
Introspective Visuomotor Control: Exploiting Uncertainty in Deep Visuomotor Control for Failure Recovery—0
Learning to Simulate on Sparse Trajectory Data—0
Learning from Imperfect Demonstrations from Agents with Varying DynamicsCode0
Optimism is All You Need: Model-Based Imitation Learning From Observation Alone—0
Variational Model-Based Imitation Learning in High-Dimensional Observation Spaces—0
Domain-Robust Visual Imitation Learning with Mutual Information ConstraintsCode1
Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation GapCode1
Decision Making in Monopoly using a Hybrid Deep Reinforcement Learning Approach—0
Generalization Through Hand-Eye Coordination: An Action Space for Learning Spatially-Invariant Visuomotor Control—0
Efficient and Interpretable Robot Manipulation with Graph Neural Networks—0
Provably Breaking the Quadratic Error Compounding Barrier in Imitation Learning, Optimally—0
Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approach—0
Imitation Learning with Human Eye Gaze via Multi-Objective PredictionCode0
Off-Policy Imitation Learning from ObservationsCode1
MobILE: Model-Based Imitation Learning From Observation AloneCode0
Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task HierarchyCode0
On the Sample Complexity of Stability Constrained Imitation Learning—0
Fully General Online Imitation Learning—0
End-to-End Egospheric Spatial MemoryCode1
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