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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 1301–1325 of 2122 papers

TitleStatusHype
Diverse Imitation Learning via Self-OrganizingGenerative Models—0
Automatic Discovery and Description of Human Planning StrategiesCode0
Mitigation of Adversarial Policy Imitation via Constrained Randomization of Policy (CRoP)—0
Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulation—0
Learning to Superoptimize Real-world Programs—0
SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies—0
Dynamic Modeling of Hand-Object Interactions via Tactile Sensing.—0
Learning Relative Interactions through Imitation—0
CLIPort: What and Where Pathways for Robotic ManipulationCode1
Imitation Learning of Stabilizing Policies for Nonlinear SystemsCode0
Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC—0
ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning—0
Deep Visual Navigation under Partial Observability—0
Reactive and Safe Road User Simulations using Neural Barrier CertificatesCode0
Cross Domain Robot Imitation with Invariant RepresentationCode0
Learning Selective Communication for Multi-Agent Path FindingCode1
Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation LearningCode0
NEAT: Neural Attention Fields for End-to-End Autonomous DrivingCode1
Fixing exposure bias with imitation learning needs powerful oracles—0
Dynamic Modeling of Hand-Object Interactions via Tactile Sensing—0
Reinforcement Learning for Battery Energy Storage Dispatch augmented with Model-based Optimizer—0
Error-based or target-based? A unifying framework for learning in recurrent spiking networks—0
MimicBot: Combining Imitation and Reinforcement Learning to win in Bot Bowl—0
Provably Efficient Generative Adversarial Imitation Learning for Online and Offline Setting with Linear Function Approximation—0
End-to-End Urban Driving by Imitating a Reinforcement Learning CoachCode1
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