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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 1801–1825 of 2122 papers

TitleStatusHype
CARIL: Confidence-Aware Regression in Imitation Learning for Autonomous DrivingCode0
Distance Weighted Supervised Learning for Offline Interaction DataCode0
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot CoordinationCode0
Learning for Long-Horizon Planning via Neuro-Symbolic Abductive ImitationCode0
Online Adaptation for Enhancing Imitation Learning PoliciesCode0
Reward-Conditioned PoliciesCode0
Learning Belief Representations for Imitation Learning in POMDPsCode0
Learning Beam Search Policies via Imitation LearningCode0
Combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging taskCode0
Learning from Imperfect Demonstrations from Agents with Varying DynamicsCode0
Online Baum-Welch algorithm for Hierarchical Imitation LearningCode0
Online Cascade Learning for Efficient Inference over StreamsCode0
Learning from Trajectories via Subgoal DiscoveryCode0
Learning Generalizable 3D Manipulation With 10 DemonstrationsCode0
Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic GraspingCode0
Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation LearningCode0
Contractive Dynamical Imitation Policies for Efficient Out-of-Sample RecoveryCode0
Reward learning from human preferences and demonstrations in AtariCode0
Learning How to Actively Learn: A Deep Imitation Learning ApproachCode0
Learning human behaviors from motion capture by adversarial imitationCode0
Learning from Mistakes via Cooperative Study Assistant for Large Language ModelsCode0
Learning Latent Process from High-Dimensional Event Sequences via Efficient SamplingCode0
Confidence-Guided Human-AI Collaboration: Reinforcement Learning with Distributional Proxy Value Propagation for Autonomous DrivingCode0
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image TranslationCode0
Learning Memory Mechanisms for Decision Making through DemonstrationsCode0
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