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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 1451–1500 of 2122 papers

TitleStatusHype
Learning Lipschitz Feedback Policies from Expert Demonstrations: Closed-Loop Guarantees, Generalization and Robustness—0
Learning Robust Rewards with Adverserial Inverse Reinforcement Learning—0
Learning Self-Imitating Diverse Policies—0
Learning Sensorimotor Primitives of Sequential Manipulation Tasks from Visual Demonstrations—0
Learning Soft Driving Constraints from Vectorized Scene Embeddings while Imitating Expert Trajectories—0
Learning Solution Manifolds for Control Problems via Energy Minimization—0
Learning Space-Time Semantic Correspondences—0
Learning Stable Koopman Embeddings for Identification and Control—0
Learning Strategy Representation for Imitation Learning in Multi-Agent Games—0
Learning Task-Agnostic Skill Bases to Uncover Motor Primitives in Animal Behaviors—0
Learning Task Decomposition with Order-Memory Policy Network—0
Learning Temporal Strategic Relationships using Generative Adversarial Imitation Learning—0
Learning the optimal state-feedback via supervised imitation learning—0
Learning the Representation of Behavior Styles with Imitation Learning—0
Learning to Actively Learn Neural Machine Translation—0
Learning to Compensate Photovoltaic Power Fluctuations from Images of the Sky by Imitating an Optimal Policy—0
Learning To Cut By Looking Ahead: Cutting Plane Selection via Imitation Learning—0
Learning to Defend by Learning to Attack—0
Learning to Discern: Imitating Heterogeneous Human Demonstrations with Preference and Representation Learning—0
Learning to Drive Anywhere—0
Learning to Drive by Imitating Surrounding Vehicles—0
Learning to Drive by Observing the Best and Synthesizing the Worst—0
Learning to Dynamically Coordinate Multi-Robot Teams in Graph Attention Networks—0
Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research—0
Learning to Gather Information via Imitation—0
Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations—0
Learning to Infer User Interface Attributes from Images—0
Learning to Interactively Learn and Assist—0
Learning to Make Decisions via Submodular Regularization—0
Learning to Multi-Task Learn for Better Neural Machine Translation—0
Learning to Optimize in Model Predictive Control—0
Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning—0
Learning to Prove Trigonometric Identities—0
Learning to Reach Goals Without Reinforcement Learning—0
Learning to Reason in Large Theories without Imitation—0
Learning to request guidance in emergent language—0
Learning to Request Guidance in Emergent Communication—0
Learning to Search for Fast Maximum Common Subgraph Detection—0
Learning to Search in Branch and Bound Algorithms—0
Learning to Search via Retrospective Imitation—0
Learning to Select Nodes in Bounded Suboptimal Conflict-Based Search for Multi-Agent Path Finding—0
Learning to Simulate on Sparse Trajectory Data—0
Learning to Structure an Image with Few Colors and Beyond—0
Learning to Superoptimize Real-world Programs—0
Learning to Transfer In-Hand Manipulations Using a Greedy Shape Curriculum—0
Learning Translations: Emergent Communication Pretraining for Cooperative Language Acquisition—0
Learning Value Functions from Undirected State-only Experience—0
Learning Vision-based Flight in Drone Swarms by Imitation—0
Learning What's Easy: Fully Differentiable Neural Easy-First Taggers—0
Learning with Imperfect Models: When Multi-step Prediction Mitigates Compounding Error—0
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