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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 901–925 of 2122 papers

TitleStatusHype
Graph-based Prediction and Planning Policy Network (GP3Net) for scalable self-driving in dynamic environments using Deep Reinforcement Learning—0
Language-Conditioned Semantic Search-Based Policy for Robotic Manipulation Tasks—0
Backward Learning for Goal-Conditioned PoliciesCode0
Understanding Representations Pretrained with Auxiliary Losses for Embodied Agent Planning—0
SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World—0
Visual Hindsight Self-Imitation Learning for Interactive Navigation—0
Visual Encoders for Data-Efficient Imitation Learning in Modern Video Games—0
Domain Adaptive Imitation Learning with Visual Observation—0
DeformGS: Scene Flow in Highly Deformable Scenes for Deformable Object Manipulation—0
Efficient Model-Based Concave Utility Reinforcement Learning through Greedy Mirror Descent—0
Toward a Surgeon-in-the-Loop Ophthalmic Robotic Apprentice using Reinforcement and Imitation LearningCode0
Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges—0
Optimal Power Flow in Highly Renewable Power System Based on Attention Neural Networks—0
Tube-NeRF: Efficient Imitation Learning of Visuomotor Policies from MPC using Tube-Guided Data Augmentation and NeRFs—0
Curriculum Learning and Imitation Learning for Model-free Control on Financial Time-series—0
RLIF: Interactive Imitation Learning as Reinforcement Learning—0
Orca 2: Teaching Small Language Models How to Reason—0
Generalizable Imitation Learning Through Pre-Trained Representations—0
Extending Multilingual Machine Translation through Imitation Learning—0
Adversarial Imitation Learning On Aggregated Data—0
UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations—0
Social Motion Prediction with Cognitive Hierarchies—0
Time-Efficient Reinforcement Learning with Stochastic Stateful Policies—0
Imitation Learning based Alternative Multi-Agent Proximal Policy Optimization for Well-Formed Swarm-Oriented Pursuit Avoidance—0
MAAIP: Multi-Agent Adversarial Interaction Priors for imitation from fighting demonstrations for physics-based characters—0
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