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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 851–900 of 2122 papers

TitleStatusHype
Deep Learning for Visual Navigation of Underwater Robots—0
Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning—0
Imitating Language via Scalable Inverse Reinforcement Learning—0
Adversarial Imitation Learning via Random Search—0
Imitation with Neural Density Models—0
Imitating Past Successes can be Very Suboptimal—0
SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World—0
Imitating Task and Motion Planning with Visuomotor Transformers—0
ExACT: An End-to-End Autonomous Excavator System Using Action Chunking With Transformers—0
Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation—0
Imitation Bootstrapped Reinforcement Learning—0
Imitation by Predicting Observations—0
CNN-based Game State Detection for a Foosball Table—0
Evolution of cooperation in the public goods game with Q-learning—0
Imitation from Diverse Behaviors: Wasserstein Quality Diversity Imitation Learning with Single-Step Archive Exploration—0
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch—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
Imitation Learning as f-Divergence Minimization—0
Evolutionary Selective Imitation: Interpretable Agents by Imitation Learning Without a Demonstrator—0
CLUZH at SIGMORPHON 2021 Shared Task on Multilingual Grapheme-to-Phoneme Conversion: Variations on a Baseline—0
Asking for Help: Failure Prediction in Behavioral Cloning through Value Approximation—0
Imitation Learning based Alternative Multi-Agent Proximal Policy Optimization for Well-Formed Swarm-Oriented Pursuit Avoidance—0
CLUZH at SIGMORPHON 2020 Shared Task on Multilingual Grapheme-to-Phoneme Conversion—0
Imitation Learning by Coaching—0
Event Extraction with Generative Adversarial Imitation Learning—0
Evaluation metrics for behaviour modeling—0
CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning—0
Asking Before Acting: Gather Information in Embodied Decision Making with Language Models—0
Imitation Learning for Adaptive Control of a Virtual Soft Exoglove—0
Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning—0
Imitation Learning for Autonomous Trajectory Learning of Robot Arms in Space—0
Imitation Learning for End to End Vehicle Longitudinal Control with Forward Camera—0
Adversarial Imitation Learning via Boosting—0
Imitation Learning for Fashion Style Based on Hierarchical Multimodal Representation—0
Imitation Learning for High Precision Peg-in-Hole Tasks—0
Imitation Learning for Human Pose Prediction—0
Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding—0
Imitation learning for language generation from unaligned data—0
Evaluation Function Approximation for Scrabble—0
Imitation Learning for Neural Network Autopilot in Fixed-Wing Unmanned Aerial Systems—0
Imitation Learning for Non-Autoregressive Neural Machine Translation—0
Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approach—0
Cloud-Based Hierarchical Imitation Learning for Scalable Transfer of Construction Skills from Human Workers to Assisting Robots—0
Imitation learning for structured prediction in natural language processing—0
Imitation Learning for Vision-based Lane Keeping Assistance—0
Deterministic Policy Imitation Gradient Algorithm—0
Imitation Learning from Imperfect Demonstration—0
A Simple Imitation Learning Method via Contrastive Regularization—0
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