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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 2026–2050 of 2122 papers

TitleStatusHype
Imitating Language via Scalable Inverse Reinforcement Learning—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
Imitation-Based Active Camera Control with Deep Convolutional Neural Network—0
Imitation Bootstrapped Reinforcement Learning—0
Imitation by Predicting Observations—0
Imitation from Diverse Behaviors: Wasserstein Quality Diversity Imitation Learning with Single-Step Archive Exploration—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
Imitation Learning As State Matching via Differentiable Physics—0
A Survey of Imitation Learning Methods, Environments and Metrics—0
Imitation Learning based Alternative Multi-Agent Proximal Policy Optimization for Well-Formed Swarm-Oriented Pursuit Avoidance—0
Forward and inverse reinforcement learning sharing network weights and hyperparameters—0
Imitation Learning by Coaching—0
Imitation Learning Datasets: A Toolkit For Creating Datasets, Training Agents and Benchmarking—0
Imitation Learning for Adaptive Control of a Virtual Soft Exoglove—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
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
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