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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 1576–1600 of 2122 papers

TitleStatusHype
Avoidance Learning Using Observational Reinforcement Learning—0
Avoidance of Manual Labeling in Robotic Autonomous Navigation Through Multi-Sensory Semi-Supervised Learning—0
Back to Reality for Imitation Learning—0
Batch Recurrent Q-Learning for Backchannel Generation Towards Engaging Agents—0
Bayesian Imitation Learning for End-to-End Mobile Manipulation—0
Bayesian Learning for Dynamic Inference—0
Bayesian Multi-type Mean Field Multi-agent Imitation Learning—0
BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning—0
BEAC: Imitating Complex Exploration and Task-oriented Behaviors for Invisible Object Nonprehensile Manipulation—0
BEAST: Efficient Tokenization of B-Splines Encoded Action Sequences for Imitation Learning—0
Behavioral Cloning from Noisy Demonstrations—0
Error-based or target-based? A unifying framework for learning in recurrent spiking networks—0
Behavioral Cloning via Search in Video PreTraining Latent Space—0
Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets—0
Behavior-Targeted Attack on Reinforcement Learning with Limited Access to Victim's Policy—0
Behavioural Cloning in VizDoom—0
Improving Behavioural Cloning with Positive Unlabeled Learning—0
Bellman Diffusion Models—0
Benchmarking Mobile Device Control Agents across Diverse Configurations—0
Benchmarking Sample Selection Strategies for Batch Reinforcement Learning—0
BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay—0
Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration—0
Bi-LAT: Bilateral Control-Based Imitation Learning via Natural Language and Action Chunking with Transformers—0
Blending Imitation and Reinforcement Learning for Robust Policy Improvement—0
Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning—0
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