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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 1826–1850 of 2122 papers

TitleStatusHype
Embedding Contextual Information through Reward Shaping in Multi-Agent Learning: A Case Study from Google Football—0
Embedding Symbolic Temporal Knowledge into Deep Sequential Models—0
Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula—0
Emergence of cooperation under punishment: A reinforcement learning perspective—0
Emergent Agentic Transformer from Chain of Hindsight Experience—0
EMPERROR: A Flexible Generative Perception Error Model for Probing Self-Driving Planners—0
Empirical Analysis of Sim-and-Real Cotraining Of Diffusion Policies For Planar Pushing from Pixels—0
End-to-End Deep Imitation Learning: Robot Soccer Case Study—0
End-to-End Differentiable Adversarial Imitation Learning—0
End-to-end driving simulation via angle branched network—0
End-to-End Driving via Self-Supervised Imitation Learning Using Camera and LiDAR Data—0
End-to-End Imitation Learning for Optimal Asteroid Proximity Operations—0
End-to-end Manipulator Calligraphy Planning via Variational Imitation Learning—0
Deep Visual Navigation under Partial Observability—0
End-to-End Stable Imitation Learning via Autonomous Neural Dynamic Policies—0
End-to-End Steering for Autonomous Vehicles via Conditional Imitation Co-Learning—0
Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning—0
EnerVerse-AC: Envisioning Embodied Environments with Action Condition—0
Enhanced DACER Algorithm with High Diffusion Efficiency—0
Enhanced Generalization through Prioritization and Diversity in Self-Imitation Reinforcement Learning over Procedural Environments with Sparse Rewards—0
Enhancing Autonomous Driving Safety with Collision Scenario Integration—0
Enhancing Reusability of Learned Skills for Robot Manipulation via Gaze and Bottleneck—0
Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning—0
EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning—0
Entity-Centric Coreference Resolution with Model Stacking—0
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