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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 951–975 of 2122 papers

TitleStatusHype
Improving Generative Adversarial Imitation Learning with Non-expert Demonstrations—0
Alibaba’s Submission for the WMT 2020 APE Shared Task: Improving Automatic Post-Editing with Pre-trained Conditional Cross-Lingual BERT—0
Improving Learning from Demonstrations by Learning from Experience—0
Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback—0
GymFG: A Framework with a Gym Interface for FlightGear—0
Improving Retrospective Language Agents via Joint Policy Gradient Optimization—0
Improving Sequential Recommendation Consistency with Self-Supervised Imitation—0
Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight—0
Curriculum Offline Imitating Learning—0
Curriculum Learning and Imitation Learning for Model-free Control on Financial Time-series—0
Incremental Learning of Retrievable Skills For Efficient Continual Task Adaptation—0
Infer and Adapt: Bipedal Locomotion Reward Learning from Demonstrations via Inverse Reinforcement Learning—0
Guided Meta-Policy Search—0
Guided Imitation of Task and Motion Planning—0
Infinite-Horizon Differentiable Model Predictive Control—0
Autoverse: An Evolvable Game Language for Learning Robust Embodied Agents—0
Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning—0
GRP Model for Sensorimotor Learning—0
Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation—0
Initial State Interventions for Deconfounded Imitation Learning—0
Grounding Language Plans in Demonstrations Through Counterfactual Perturbations—0
Language Conditioned Imitation Learning over Unstructured Data—0
Inspiration Learning through Preferences—0
Instant Policy: In-Context Imitation Learning via Graph Diffusion—0
Curating Demonstrations using Online Experience—0
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