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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 351–375 of 2122 papers

TitleStatusHype
Learning non-Markovian Decision-Making from State-only SequencesCode0
Learning One-Shot Imitation from Humans without HumansCode0
Learning on One Mode: Addressing Multi-Modality in Offline Reinforcement LearningCode0
Learning Latent Process from High-Dimensional Event Sequences via Efficient SamplingCode0
Bootstrapping Linear Models for Fast Online Adaptation in Human-Agent CollaborationCode0
Learning Memory Mechanisms for Decision Making through DemonstrationsCode0
Efficient Motion Planning for Automated Lane Change based on Imitation Learning and Mixed-Integer OptimizationCode0
Learning Representative Trajectories of Dynamical Systems via Domain-Adaptive ImitationCode0
Learning to Generalize for Sequential Decision MakingCode0
Learning from Imperfect Demonstrations from Agents with Varying DynamicsCode0
Combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging taskCode0
Learning from Trajectories via Subgoal DiscoveryCode0
Learning Generalizable 3D Manipulation With 10 DemonstrationsCode0
Bipedal Walking Robot using Deep Deterministic Policy GradientCode0
An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase GenerationCode0
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot CoordinationCode0
BiKC: Keypose-Conditioned Consistency Policy for Bimanual Robotic ManipulationCode0
Learning for Long-Horizon Planning via Neuro-Symbolic Abductive ImitationCode0
Learning How to Actively Learn: A Deep Imitation Learning ApproachCode0
Beyond spiking networks: the computational advantages of dendritic amplification and input segregationCode0
Beyond Imitation: Learning Key Reasoning Steps from Dual Chain-of-Thoughts in Reasoning DistillationCode0
Learning Belief Representations for Imitation Learning in POMDPsCode0
Learning Calibratable Policies using Programmatic Style-ConsistencyCode0
Learning Visuomotor Policies for Aerial Navigation Using Cross-Modal RepresentationsCode0
Learning from Mistakes via Cooperative Study Assistant for Large Language ModelsCode0
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