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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 1626–1650 of 2122 papers

TitleStatusHype
Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach—0
Causal Transfer for Imitation Learning and Decision Making under Sensor-shift—0
CBIL: Collective Behavior Imitation Learning for Fish from Real Videos—0
CASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters—0
CCDP: Composition of Conditional Diffusion Policies with Guided Sampling—0
CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories—0
CEIL: Generalized Contextual Imitation Learning—0
CenterLineDet: CenterLine Graph Detection for Road Lanes with Vehicle-mounted Sensors by Transformer for HD Map Generation—0
Decentralized Multi-Agents by Imitation of a Centralized Controller—0
C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory—0
CGD: Constraint-Guided Diffusion Policies for UAV Trajectory Planning—0
Chain of Thought Imitation with Procedure Cloning—0
Challenging Common Assumptions in Convex Reinforcement Learning—0
ChicGrasp: Imitation-Learning based Customized Dual-Jaw Gripper Control for Delicate, Irregular Bio-products Manipulation—0
CIMRL: Combining IMitation and Reinforcement Learning for Safe Autonomous Driving—0
CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving—0
CIVIL: Causal and Intuitive Visual Imitation Learning—0
CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations—0
On the Sample Complexity of Stability Constrained Imitation Learning—0
Cloud-Based Hierarchical Imitation Learning for Scalable Transfer of Construction Skills from Human Workers to Assisting Robots—0
CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning—0
CLUZH at SIGMORPHON 2020 Shared Task on Multilingual Grapheme-to-Phoneme Conversion—0
CLUZH at SIGMORPHON 2021 Shared Task on Multilingual Grapheme-to-Phoneme Conversion: Variations on a Baseline—0
CNN-based Game State Detection for a Foosball Table—0
CNT (Conditioning on Noisy Targets): A new Algorithm for Leveraging Top-Down Feedback—0
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