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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 1351–1360 of 2122 papers

TitleStatusHype
JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning—0
Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning—0
Guided Imitation of Task and Motion Planning—0
Organ localisation using supervised and semi supervised approaches combining reinforcement learning with imitation learning—0
MDPFuzz: Testing Models Solving Markov Decision Processes—0
Stage Conscious Attention Network (SCAN) : A Demonstration-Conditioned Policy for Few-Shot Imitation—0
Quantile Filtered Imitation Learning—0
Distributionally Robust Imitation Learning—0
Generalizable Imitation Learning from Observation via Inferring Goal Proximity—0
Curriculum Offline Imitating Learning—0
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