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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 351–400 of 2122 papers

TitleStatusHype
Bootstrapping Linear Models for Fast Online Adaptation in Human-Agent CollaborationCode0
Efficient Motion Planning for Automated Lane Change based on Imitation Learning and Mixed-Integer OptimizationCode0
LHManip: A Dataset for Long-Horizon Language-Grounded Manipulation Tasks in Cluttered Tabletop EnvironmentsCode0
See What the Robot Can't See: Learning Cooperative Perception for Visual NavigationCode0
Learning to Guide and to Be Guided in the Architect-Builder ProblemCode0
Learning to Generalize for Sequential Decision MakingCode0
Learning to Stabilize High-dimensional Unknown Systems Using Lyapunov-guided ExplorationCode0
Bipedal Walking Robot using Deep Deterministic Policy GradientCode0
An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase GenerationCode0
Learning to Accelerate Approximate Methods for Solving Integer Programming via Early FixingCode0
BiKC: Keypose-Conditioned Consistency Policy for Bimanual Robotic ManipulationCode0
Learning to Build by Building Your Own InstructionsCode0
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 Self-Correctable Policies and Value Functions from Demonstrations with Negative SamplingCode0
Learning Sparse Rewarded Tasks from Sub-Optimal DemonstrationsCode0
Learning One-Shot Imitation from Humans without HumansCode0
Learning on One Mode: Addressing Multi-Modality in Offline Reinforcement LearningCode0
Learning non-Markovian Decision-Making from State-only SequencesCode0
Learning Representative Trajectories of Dynamical Systems via Domain-Adaptive ImitationCode0
Learning Speed-Adaptive Walking Agent Using Imitation Learning with Physics-Informed SimulationCode0
Learning How to Actively Learn: A Deep Imitation Learning ApproachCode0
Amplifying the Imitation Effect for Reinforcement Learning of UCAV's Mission ExecutionCode0
Learning human behaviors from motion capture by adversarial imitationCode0
Learning from Trajectories via Subgoal DiscoveryCode0
Learning Generalizable 3D Manipulation With 10 DemonstrationsCode0
Combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging taskCode0
Accept Synthetic Objects as Real: End-to-End Training of Attentive Deep Visuomotor Policies for Manipulation in ClutterCode0
Learning from Imperfect Demonstrations from Agents with Varying DynamicsCode0
Learning Latent Process from High-Dimensional Event Sequences via Efficient SamplingCode0
BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoTCode0
Learning Visuomotor Policies for Aerial Navigation Using Cross-Modal RepresentationsCode0
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot CoordinationCode0
Learning Beam Search Policies via Imitation LearningCode0
Learning Belief Representations for Imitation Learning in POMDPsCode0
Bayesian Robust Optimization for Imitation LearningCode0
Bayesian Nonparametrics for Offline Skill DiscoveryCode0
Learning Calibratable Policies using Programmatic Style-ConsistencyCode0
Learning for Long-Horizon Planning via Neuro-Symbolic Abductive ImitationCode0
Learning Memory Mechanisms for Decision Making through DemonstrationsCode0
LASIL: Learner-Aware Supervised Imitation Learning For Long-term Microscopic Traffic SimulationCode0
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement LearningCode0
Backward Learning for Goal-Conditioned PoliciesCode0
Iterative Document-level Information Extraction via Imitation LearningCode0
Iterative Sizing Field Prediction for Adaptive Mesh Generation From Expert DemonstrationsCode0
Inverse Q-Learning Done Right: Offline Imitation Learning in Q^π-Realizable MDPsCode0
Inverse Reinforcement Learning by Estimating Expertise of DemonstratorsCode0
Interactive incremental learning of generalizable skills with local trajectory modulationCode0
Interactive Learning from Activity DescriptionCode0
Interactive Imitation Learning in State-SpaceCode0
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