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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 551–600 of 2122 papers

TitleStatusHype
Interactive Imitation Learning in State-SpaceCode0
Interactive Learning from Activity DescriptionCode0
IN-RIL: Interleaved Reinforcement and Imitation Learning for Policy Fine-TuningCode0
InfoGAIL: Interpretable Imitation Learning from Visual DemonstrationsCode0
Information Maximizing Curriculum: A Curriculum-Based Approach for Imitating Diverse SkillsCode0
Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and InvestigationCode0
Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task HierarchyCode0
Improving In-Context Learning with Reasoning DistillationCode0
Improving Policy Optimization with Generalist-Specialist LearningCode0
Imitrob: Imitation Learning Dataset for Training and Evaluating 6D Object Pose EstimatorsCode0
Adversarial Imitation Learning from Incomplete DemonstrationsCode0
Imitation Learning with Limited Actions via Diffusion Planners and Deep Koopman ControllersCode0
Improved Policy Optimization for Online Imitation LearningCode0
Imitation Learning of Stabilizing Policies for Nonlinear SystemsCode0
End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learningCode0
Imitation Learning of Agenda-based Semantic ParsersCode0
Imitation Learning from Purified DemonstrationsCode0
End-to-end Driving via Conditional Imitation LearningCode0
Towards Interactive Training of Non-Player Characters in Video GamesCode0
Imitation Learning from Suboptimal Demonstrations via Meta-Learning An Action RankerCode0
Imitation learning with artificial neural networks for demand response with a heuristic control approach for heat pumpsCode0
Inferring Versatile Behavior from Demonstrations by Matching Geometric DescriptorsCode0
Learning for Long-Horizon Planning via Neuro-Symbolic Abductive ImitationCode0
Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware PerspectiveCode0
Imitation Learning for Generalizable Self-driving Policy with Sim-to-real TransferCode0
Imitation Learning for Intra-Day Power Grid Operation through Topology ActionsCode0
Imitation Learning by State-Only Distribution MatchingCode0
Imitation Learning by Reinforcement LearningCode0
Imitation Learning for Autonomous Driving: Insights from Real-World TestingCode0
A Reinforcement Learning Approach for Robotic Unloading from Visual ObservationsCode0
Imitation Learning for Neural Morphological String TransductionCode0
Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context TranslationCode0
Imitation Learning-based Implicit Semantic-aware Communication Networks: Multi-layer Representation and Collaborative ReasoningCode0
End-to-end Sketch-Guided Path Planning through Imitation Learning for Autonomous Mobile RobotsCode0
Imitation Learning for Sentence Generation with Dilated Convolutions Using Adversarial TrainingCode0
Imitating Cost-Constrained Behaviors in Reinforcement LearningCode0
IIFL: Implicit Interactive Fleet Learning from Heterogeneous Human SupervisorsCode0
ImitAL: Learning Active Learning Strategies from Synthetic DataCode0
Imitating Driver Behavior with Generative Adversarial NetworksCode0
Improving End-to-End Speech Translation by Imitation-Based Knowledge Distillation with Synthetic TranscriptsCode0
IALE: Imitating Active Learner EnsemblesCode0
Causal Navigation by Continuous-time Neural NetworksCode0
ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the WorstCode0
Enhancing Online Reinforcement Learning with Meta-Learned Objective from Offline DataCode0
Integrating Reinforcement Learning, Action Model Learning, and Numeric Planning for Tackling Complex TasksCode0
Enhancing Robot Learning through Learned Human-Attention Feature MapsCode0
CILP: Co-simulation based Imitation Learner for Dynamic Resource Provisioning in Cloud Computing EnvironmentsCode0
Agnostic Interactive Imitation Learning: New Theory and Practical AlgorithmsCode0
Hybrid system identification using switching density networksCode0
Imitation Learning from a Single Temporally Misaligned VideoCode0
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