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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 1476–1500 of 2122 papers

TitleStatusHype
What data do we need for training an AV motion planner?—0
Provable Representation Learning for Imitation with Contrastive Fourier Features—0
Hyperparameter Selection for Imitation Learning—0
VISITRON: Visual Semantics-Aligned Interactively Trained Object-NavigatorCode0
From Motor Control to Team Play in Simulated Humanoid Football—0
Language Understanding for Field and Service Robots in a Priori Unknown Environments—0
Cross-domain Imitation from Observations—0
VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigation—0
Explainable Hierarchical Imitation Learning for Robotic Drink Pouring—0
Make Bipedal Robots Learn How to Imitate—0
Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration—0
RAIL: A modular framework for Reinforcement-learning-based Adversarial Imitation Learning—0
Imitation Learning via Simultaneous Optimization of Policies and Auxiliary Trajectories—0
Learning Algorithms for Regenerative Stopping Problems with Applications to Shipping Consolidation in Logistics—0
On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning—0
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
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
Data-Driven Simulation of Ride-Hailing Services using Imitation and Reinforcement Learning—0
No Need for Interactions: Robust Model-Based Imitation Learning using Neural ODECode0
Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic GraspingCode0
UAV-Assisted Communication in Remote Disaster Areas using Imitation Learning—0
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