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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 851–875 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
Asking for Help: Failure Prediction in Behavioral Cloning through Value Approximation—0
Imitating Opponent to Win: Adversarial Policy Imitation Learning in Two-player Competitive Games—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
CLUZH at SIGMORPHON 2020 Shared Task on Multilingual Grapheme-to-Phoneme Conversion—0
Event Extraction with Generative Adversarial Imitation Learning—0
Imitation Bootstrapped Reinforcement Learning—0
Imitation by Predicting Observations—0
Evaluation metrics for behaviour modeling—0
CLUE: Calibrated Latent Guidance for Offline Reinforcement 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
Asking Before Acting: Gather Information in Embodied Decision Making with Language Models—0
Adversarial Imitation Learning via Boosting—0
Evaluation Function Approximation for Scrabble—0
Imitation Learning based Alternative Multi-Agent Proximal Policy Optimization for Well-Formed Swarm-Oriented Pursuit Avoidance—0
Cloud-Based Hierarchical Imitation Learning for Scalable Transfer of Construction Skills from Human Workers to Assisting Robots—0
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