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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 571–580 of 2122 papers

TitleStatusHype
Auto-Encoding Inverse Reinforcement Learning—0
CordViP: Correspondence-based Visuomotor Policy for Dexterous Manipulation in Real-World—0
Active Third-Person Imitation Learning—0
Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation—0
Coordinated Multi-Agent Imitation Learning—0
CooHOI: Learning Cooperative Human-Object Interaction with Manipulated Object Dynamics—0
Auto-Encoding Adversarial Imitation Learning—0
Convergence of Value Aggregation for Imitation Learning—0
Auto-bidding in real-time auctions via Oracle Imitation Learning (OIL)—0
Adversarial Safety-Critical Scenario Generation using Naturalistic Human Driving Priors—0
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