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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 1676–1700 of 2122 papers

TitleStatusHype
Organ localisation using supervised and semi supervised approaches combining reinforcement learning with imitation learning—0
Out-of-Distribution Recovery with Object-Centric Keypoint Inverse Policy for Visuomotor Imitation Learning—0
Output Feedback Tube MPC-Guided Data Augmentation for Robust, Efficient Sensorimotor Policy Learning—0
PAC Bounds for Imitation and Model-based Batch Learning of Contextual Markov Decision Processes—0
PAGAR: Taming Reward Misalignment in Inverse Reinforcement Learning-Based Imitation Learning with Protagonist Antagonist Guided Adversarial Reward—0
PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization—0
Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehicles—0
Parametrized Hierarchical Procedures for Neural Programming—0
Parental Guidance: Efficient Lifelong Learning through Evolutionary Distillation—0
Pareto Inverse Reinforcement Learning for Diverse Expert Policy Generation—0
Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing—0
Partial Simulation for Imitation Learning—0
Path Integral Networks: End-to-End Differentiable Optimal Control—0
Path Planning based on 2D Object Bounding-box—0
Transferring Foundation Models for Generalizable Robotic Manipulation—0
PEAR: Primitive enabled Adaptive Relabeling for boosting Hierarchical Reinforcement Learning—0
Penalty-Based Imitation Learning With Cross Semantics Generation Sensor Fusion for Autonomous Driving—0
PERIL: Probabilistic Embeddings for hybrid Meta-Reinforcement and Imitation Learning—0
Periodic DMP formulation for Quaternion Trajectories—0
Physics-Based Dexterous Manipulations with Estimated Hand Poses and Residual Reinforcement Learning—0
Physics-informed Neural Motion Planning on Constraint Manifolds—0
PICO: Primitive Imitation for COntrol—0
PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving—0
Plan Arithmetic: Compositional Plan Vectors for Multi-Task Control—0
Planning with RL and episodic-memory behavioral priors—0
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