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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 1301–1350 of 2122 papers

TitleStatusHype
Towards Fusing Point Cloud and Visual Representations for Imitation Learning—0
Towards Human-Like Driving: Active Inference in Autonomous Vehicle Control—0
Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds—0
Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning—0
Towards More Generalizable One-shot Visual Imitation Learning—0
Towards Scalable Imitation Learning for Multi-Agent Systems with Graph Neural Networks—0
Toward the Fundamental Limits of Imitation Learning—0
Tracking the Race Between Deep Reinforcement Learning and Imitation Learning -- Extended Version—0
TRAIL: Near-Optimal Imitation Learning with Suboptimal Data—0
Training Electric Vehicle Charging Controllers with Imitation Learning—0
Training Neural Network Controllers Using Control Barrier Functions in the Presence of Disturbances—0
Training Robots without Robots: Deep Imitation Learning for Master-to-Robot Policy Transfer—0
Trajectory VAE for multi-modal imitation—0
Transferable Reward Learning by Dynamics-Agnostic Discriminator Ensemble—0
Transfering Hierarchical Structure with Dual Meta Imitation Learning—0
Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges—0
Transferring Hierarchical Structure with Dual Meta Imitation Learning—0
Transfer RL via the Undo Maps Formalism—0
Transformer-based deep imitation learning for dual-arm robot manipulation—0
Transformers for One-Shot Visual Imitation—0
Translating Natural Language Instructions to Computer Programs for Robot Manipulation—0
Transporters with Visual Foresight for Solving Unseen Rearrangement Tasks—0
Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets—0
Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learning—0
Tube-NeRF: Efficient Imitation Learning of Visuomotor Policies from MPC using Tube-Guided Data Augmentation and NeRFs—0
UAD: Unsupervised Affordance Distillation for Generalization in Robotic Manipulation—0
UAV-Assisted Communication in Remote Disaster Areas using Imitation Learning—0
UAV-Flow Colosseo: A Real-World Benchmark for Flying-on-a-Word UAV Imitation Learning—0
UCL+Sheffield at SemEval-2016 Task 8: Imitation learning for AMR parsing with an alpha-bound—0
Unbiased learning with State-Conditioned Rewards in Adversarial Imitation Learning—0
Uncertainty-Aware Data Aggregation for Deep Imitation Learning—0
Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives—0
UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations—0
Understanding Adversarial Imitation Learning in Small Sample Regime: A Stage-coupled Analysis—0
Understanding Hindsight Goal Relabeling from a Divergence Minimization Perspective—0
Understanding Human Behaviors in Crowds by Imitating the Decision-Making Process—0
Understanding Representations Pretrained with Auxiliary Losses for Embodied Agent Planning—0
Understanding the Spread of COVID-19 Epidemic: A Spatio-Temporal Point Process View—0
Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets—0
Unpacking the Individual Components of Diffusion Policy—0
Unraveling the ARC Puzzle: Mimicking Human Solutions with Object-Centric Decision Transformer—0
Adaptive Input Estimation in Linear Dynamical Systems with Applications to Learning-from-Observations—0
Unsupervised Perceptual Rewards for Imitation Learning—0
Unveiling Imitation Learning: Exploring the Impact of Data Falsity to Large Language Model—0
Unveiling the Role of Expert Guidance: A Comparative Analysis of User-centered Imitation Learning and Traditional Reinforcement Learning—0
Urban Driving with Conditional Imitation Learning—0
Using Bayesian Dynamical Systems for Motion Template Libraries—0
Using Enhanced Gaussian Cross-Entropy in Imitation Learning to Digging the First Diamond in Minecraft—0
Utilizing Eye Gaze to Enhance the Generalization of Imitation Networks to Unseen Environments—0
UZH at CoNLL--SIGMORPHON 2018 Shared Task on Universal Morphological Reinflection—0
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