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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 861–870 of 2122 papers

TitleStatusHype
Back to Reality for Imitation Learning—0
Imitation by Predicting Observations—0
Hierarchical Reinforcement Learning for Multi-agent MOBA Game—0
Data-driven Traffic Simulation: A Comprehensive Review—0
Imitation from Diverse Behaviors: Wasserstein Quality Diversity Imitation Learning with Single-Step Archive Exploration—0
Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving—0
Imitation Game: A Model-based and Imitation Learning Deep Reinforcement Learning Hybrid—0
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios—0
Imitation Learning Approach for AI Driving Olympics Trained on Real-world and Simulation Data Simultaneously—0
Data-Driven Simulation of Ride-Hailing Services using Imitation and Reinforcement Learning—0
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